diff --git a/stubtest.txt b/stubtest.txt
deleted file mode 100644
index 464ae193..00000000
--- a/stubtest.txt
+++ /dev/null
@@ -1,2510 +0,0 @@
-scipy.LowLevelCallable.__getitem__(B variable differs from runtime type def (self: Untyped, idx: Untyped) -> Untyped
-scipy._lib._boost_utils(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy._lib._boost_utils'
-scipy._lib._ccallback.CFFIFuncP(B is not present at runtime
-scipy._lib._ccallback.CFFIVoidP(B is not present at runtime
-scipy._lib._ccallback.LowLevelCallable.__getitem__(B variable differs from runtime type def (self: Untyped, idx: Untyped) -> Untyped
-scipy._lib._ccallback.PyCFuncPtr(B cannot be subclassed at runtime, but isn't marked with @final in the stub
-scipy._lib._highs_utils(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy._lib._highs_utils'
-scipy._lib._pep440.Infinity(B is not a type
-scipy._lib._pep440.NegativeInfinity(B is not a type
-scipy._lib._unuran_utils(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy._lib._unuran_utils'
-scipy._lib._util.Generator.__init__(B is inconsistent, stub does not have *args argument "args"
-scipy._lib._util.Generator.beta(B is not present in stub
-scipy._lib._util.Generator.binomial(B is not present in stub
-scipy._lib._util.Generator.bit_generator(B is not present in stub
-scipy._lib._util.Generator.bytes(B is not present in stub
-scipy._lib._util.Generator.chisquare(B is not present in stub
-scipy._lib._util.Generator.choice(B is not present in stub
-scipy._lib._util.Generator.dirichlet(B is not present in stub
-scipy._lib._util.Generator.exponential(B is not present in stub
-scipy._lib._util.Generator.f(B is not present in stub
-scipy._lib._util.Generator.gamma(B is not present in stub
-scipy._lib._util.Generator.geometric(B is not present in stub
-scipy._lib._util.Generator.gumbel(B is not present in stub
-scipy._lib._util.Generator.hypergeometric(B is not present in stub
-scipy._lib._util.Generator.integers(B is not present in stub
-scipy._lib._util.Generator.laplace(B is not present in stub
-scipy._lib._util.Generator.logistic(B is not present in stub
-scipy._lib._util.Generator.lognormal(B is not present in stub
-scipy._lib._util.Generator.logseries(B is not present in stub
-scipy._lib._util.Generator.multinomial(B is not present in stub
-scipy._lib._util.Generator.multivariate_hypergeometric(B is not present in stub
-scipy._lib._util.Generator.multivariate_normal(B is not present in stub
-scipy._lib._util.Generator.negative_binomial(B is not present in stub
-scipy._lib._util.Generator.noncentral_chisquare(B is not present in stub
-scipy._lib._util.Generator.noncentral_f(B is not present in stub
-scipy._lib._util.Generator.normal(B is not present in stub
-scipy._lib._util.Generator.pareto(B is not present in stub
-scipy._lib._util.Generator.permutation(B is not present in stub
-scipy._lib._util.Generator.permuted(B is not present in stub
-scipy._lib._util.Generator.poisson(B is not present in stub
-scipy._lib._util.Generator.power(B is not present in stub
-scipy._lib._util.Generator.random(B is not present in stub
-scipy._lib._util.Generator.rayleigh(B is not present in stub
-scipy._lib._util.Generator.shuffle(B is not present in stub
-scipy._lib._util.Generator.spawn(B is not present in stub
-scipy._lib._util.Generator.standard_cauchy(B is not present in stub
-scipy._lib._util.Generator.standard_exponential(B is not present in stub
-scipy._lib._util.Generator.standard_gamma(B is not present in stub
-scipy._lib._util.Generator.standard_normal(B is not present in stub
-scipy._lib._util.Generator.standard_t(B is not present in stub
-scipy._lib._util.Generator.triangular(B is not present in stub
-scipy._lib._util.Generator.uniform(B is not present in stub
-scipy._lib._util.Generator.vonmises(B is not present in stub
-scipy._lib._util.Generator.wald(B is not present in stub
-scipy._lib._util.Generator.weibull(B is not present in stub
-scipy._lib._util.Generator.zipf(B is not present in stub
-scipy._lib._util.SeedType(B is not present at runtime
-scipy._lib.decorator.ContextManager.__init__(B is inconsistent, stub argument "func" differs from runtime argument "g"
-scipy.fftpack.basic.fft(B is not present in stub
-scipy.fftpack.basic.fft2(B is not present in stub
-scipy.fftpack.basic.fftn(B is not present in stub
-scipy.fftpack.basic.ifft(B is not present in stub
-scipy.fftpack.basic.ifft2(B is not present in stub
-scipy.fftpack.basic.ifftn(B is not present in stub
-scipy.fftpack.basic.irfft(B is not present in stub
-scipy.fftpack.basic.rfft(B is not present in stub
-scipy.fftpack.helper.fftfreq(B is not present in stub
-scipy.fftpack.helper.fftshift(B is not present in stub
-scipy.fftpack.helper.ifftshift(B is not present in stub
-scipy.fftpack.helper.next_fast_len(B is not present in stub
-scipy.fftpack.helper.rfftfreq(B is not present in stub
-scipy.fftpack.pseudo_diffs.cc_diff(B is not present in stub
-scipy.fftpack.pseudo_diffs.convolve(B is not present in stub
-scipy.fftpack.pseudo_diffs.cs_diff(B is not present in stub
-scipy.fftpack.pseudo_diffs.diff(B is not present in stub
-scipy.fftpack.pseudo_diffs.hilbert(B is not present in stub
-scipy.fftpack.pseudo_diffs.ihilbert(B is not present in stub
-scipy.fftpack.pseudo_diffs.itilbert(B is not present in stub
-scipy.fftpack.pseudo_diffs.sc_diff(B is not present in stub
-scipy.fftpack.pseudo_diffs.shift(B is not present in stub
-scipy.fftpack.pseudo_diffs.ss_diff(B is not present in stub
-scipy.fftpack.pseudo_diffs.tilbert(B is not present in stub
-scipy.fftpack.realtransforms.dct(B is not present in stub
-scipy.fftpack.realtransforms.dctn(B is not present in stub
-scipy.fftpack.realtransforms.dst(B is not present in stub
-scipy.fftpack.realtransforms.dstn(B is not present in stub
-scipy.fftpack.realtransforms.idct(B is not present in stub
-scipy.fftpack.realtransforms.idctn(B is not present in stub
-scipy.fftpack.realtransforms.idst(B is not present in stub
-scipy.fftpack.realtransforms.idstn(B is not present in stub
-scipy.integrate.dblquad(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],)
-scipy.integrate.dblquad(B is inconsistent, runtime argument "epsabs" is not keyword-only
-scipy.integrate.dblquad(B is inconsistent, runtime argument "epsrel" is not keyword-only
-scipy.integrate.fixed_quad(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],)
-scipy.integrate.nquad(B is inconsistent, runtime argument "full_output" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.integrate.odeint(B is inconsistent, runtime argument "col_deriv" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "full_output" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "ml" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "mu" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "rtol" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "atol" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "tcrit" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "h0" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "hmax" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "hmin" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "ixpr" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "mxstep" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "mxhnil" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "mxordn" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "mxords" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "printmessg" is not keyword-only
-scipy.integrate.odeint(B is inconsistent, runtime argument "tfirst" is not keyword-only
-scipy.integrate.quad(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.integrate.quad(B is inconsistent, runtime argument "full_output" is not keyword-only
-scipy.integrate.quad(B is inconsistent, stub does not have argument "epsabs"
-scipy.integrate.quad(B is inconsistent, stub does not have argument "epsrel"
-scipy.integrate.quad(B is inconsistent, stub does not have argument "limit"
-scipy.integrate.quad(B is inconsistent, stub does not have argument "points"
-scipy.integrate.quad(B is inconsistent, stub does not have argument "weight"
-scipy.integrate.quad(B is inconsistent, stub does not have argument "wvar"
-scipy.integrate.quad(B is inconsistent, stub does not have argument "wopts"
-scipy.integrate.quad(B is inconsistent, runtime argument "maxp1" is not keyword-only
-scipy.integrate.quad(B is inconsistent, runtime argument "limlst" is not keyword-only
-scipy.integrate.quad(B is inconsistent, runtime argument "complex_func" is not keyword-only
-scipy.integrate.quad(B is inconsistent, runtime does not have **kwargs argument "kwds"
-scipy.integrate.quad_vec(B is inconsistent, runtime argument "cache_size" has a default value of type float, which is incompatible with stub argument type int. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.integrate.quad_vec(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-3 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.integrate.tplquad(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],)
-scipy.integrate.tplquad(B is inconsistent, runtime argument "epsabs" is not keyword-only
-scipy.integrate.tplquad(B is inconsistent, runtime argument "epsrel" is not keyword-only
-scipy.integrate._bvp.BVPResult.y(B is not present at runtime
-scipy.integrate._bvp.BVPResult.yp(B is not present at runtime
-scipy.integrate._bvp.EPS(B is not present in stub
-scipy.integrate._bvp.TERMINATION_MESSAGES(B is not present in stub
-scipy.integrate._bvp.collocation_fun(B is not present in stub
-scipy.integrate._bvp.compute_jac_indices(B is not present in stub
-scipy.integrate._bvp.construct_global_jac(B is not present in stub
-scipy.integrate._bvp.create_spline(B is not present in stub
-scipy.integrate._bvp.estimate_bc_jac(B is not present in stub
-scipy.integrate._bvp.estimate_fun_jac(B is not present in stub
-scipy.integrate._bvp.estimate_rms_residuals(B is not present in stub
-scipy.integrate._bvp.modify_mesh(B is not present in stub
-scipy.integrate._bvp.prepare_sys(B is not present in stub
-scipy.integrate._bvp.print_iteration_header(B is not present in stub
-scipy.integrate._bvp.print_iteration_progress(B is not present in stub
-scipy.integrate._bvp.solve_newton(B is not present in stub
-scipy.integrate._bvp.stacked_matmul(B is not present in stub
-scipy.integrate._bvp.wrap_functions(B is not present in stub
-scipy.integrate._ivp.ivp.prepare_events(B is inconsistent, runtime does not have argument "t0"
-scipy.integrate._ode.IntegratorBase.scalar(B variable differs from runtime type type
-scipy.integrate._ode.dop853.success(B variable differs from runtime type None
-scipy.integrate._ode.dopri5.success(B variable differs from runtime type None
-scipy.integrate._ode.lsoda.success(B variable differs from runtime type None
-scipy.integrate._ode.vode.success(B variable differs from runtime type None
-scipy.integrate._ode.zvode.scalar(B variable differs from runtime type type
-scipy.integrate._ode.zvode.success(B variable differs from runtime type None
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "col_deriv" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "full_output" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "ml" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "mu" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "rtol" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "atol" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "tcrit" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "h0" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "hmax" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "hmin" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "ixpr" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "mxstep" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "mxhnil" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "mxordn" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "mxords" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "printmessg" is not keyword-only
-scipy.integrate._odepack_py.odeint(B is inconsistent, runtime argument "tfirst" is not keyword-only
-scipy.integrate._quad_vec._Bunch.integrals(B is not present at runtime
-scipy.integrate._quad_vec.quad_vec(B is inconsistent, runtime argument "cache_size" has a default value of type float, which is incompatible with stub argument type int. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.integrate._quad_vec.quad_vec(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-2 = Unpack[()]],) | (Unpack[_Ts`-3 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.integrate._quadpack_py._NQuad.full_output(B is not present at runtime
-scipy.integrate._quadpack_py._NQuad.func(B is not present at runtime
-scipy.integrate._quadpack_py._NQuad.opts(B is not present at runtime
-scipy.integrate._quadpack_py._NQuad.ranges(B is not present at runtime
-scipy.integrate._quadpack_py._OptFunc.opt(B is not present at runtime
-scipy.integrate._quadpack_py._RangeFunc.range_(B is not present at runtime
-scipy.integrate._quadpack_py.dblquad(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],)
-scipy.integrate._quadpack_py.dblquad(B is inconsistent, runtime argument "epsabs" is not keyword-only
-scipy.integrate._quadpack_py.dblquad(B is inconsistent, runtime argument "epsrel" is not keyword-only
-scipy.integrate._quadpack_py.nquad(B is inconsistent, runtime argument "full_output" is not keyword-only
-scipy.integrate._quadpack_py.quad(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],) | (Unpack[_Ts`-1 = Unpack[()]],). This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.integrate._quadpack_py.quad(B is inconsistent, runtime argument "full_output" is not keyword-only
-scipy.integrate._quadpack_py.quad(B is inconsistent, stub does not have argument "epsabs"
-scipy.integrate._quadpack_py.quad(B is inconsistent, stub does not have argument "epsrel"
-scipy.integrate._quadpack_py.quad(B is inconsistent, stub does not have argument "limit"
-scipy.integrate._quadpack_py.quad(B is inconsistent, stub does not have argument "points"
-scipy.integrate._quadpack_py.quad(B is inconsistent, stub does not have argument "weight"
-scipy.integrate._quadpack_py.quad(B is inconsistent, stub does not have argument "wvar"
-scipy.integrate._quadpack_py.quad(B is inconsistent, stub does not have argument "wopts"
-scipy.integrate._quadpack_py.quad(B is inconsistent, runtime argument "maxp1" is not keyword-only
-scipy.integrate._quadpack_py.quad(B is inconsistent, runtime argument "limlst" is not keyword-only
-scipy.integrate._quadpack_py.quad(B is inconsistent, runtime argument "complex_func" is not keyword-only
-scipy.integrate._quadpack_py.quad(B is inconsistent, runtime does not have **kwargs argument "kwds"
-scipy.integrate._quadpack_py.tplquad(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-1 = Unpack[()]],)
-scipy.integrate._quadpack_py.tplquad(B is inconsistent, runtime argument "epsabs" is not keyword-only
-scipy.integrate._quadpack_py.tplquad(B is inconsistent, runtime argument "epsrel" is not keyword-only
-scipy.integrate._quadrature.fixed_quad(B is inconsistent, runtime argument "args" has a default value of type (), which is incompatible with stub argument type (Unpack[_Ts`-2 = Unpack[()]],)
-scipy.integrate._tanhsinh.nsum(B is not present at runtime
-scipy.interpolate.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "k" is not keyword-only
-scipy.interpolate.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "ext" is not keyword-only
-scipy.interpolate.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "check_finite" is not keyword-only
-scipy.interpolate.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime does not have argument "s"
-scipy.interpolate.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "kx" is not keyword-only
-scipy.interpolate.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "ky" is not keyword-only
-scipy.interpolate.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "eps" is not keyword-only
-scipy.interpolate.LSQSphereBivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate.LSQSphereBivariateSpline.__init__(B is inconsistent, runtime argument "eps" is not keyword-only
-scipy.interpolate.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "k" is not keyword-only
-scipy.interpolate.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "ext" is not keyword-only
-scipy.interpolate.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "check_finite" is not keyword-only
-scipy.interpolate.LinAlgError(B is not present at runtime
-scipy.interpolate.RectBivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate.RectBivariateSpline.__init__(B is inconsistent, runtime argument "kx" is not keyword-only
-scipy.interpolate.RectBivariateSpline.__init__(B is inconsistent, runtime argument "ky" is not keyword-only
-scipy.interpolate.RectBivariateSpline.__init__(B is inconsistent, runtime argument "s" is not keyword-only
-scipy.interpolate.RectSphereBivariateSpline.__init__(B is inconsistent, runtime argument "pole_continuity" is not keyword-only
-scipy.interpolate.RectSphereBivariateSpline.__init__(B is inconsistent, runtime argument "pole_values" is not keyword-only
-scipy.interpolate.RectSphereBivariateSpline.__init__(B is inconsistent, runtime argument "pole_exact" is not keyword-only
-scipy.interpolate.RectSphereBivariateSpline.__init__(B is inconsistent, runtime argument "pole_flat" is not keyword-only
-scipy.interpolate.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "kx" is not keyword-only
-scipy.interpolate.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "ky" is not keyword-only
-scipy.interpolate.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "s" is not keyword-only
-scipy.interpolate.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "eps" is not keyword-only
-scipy.interpolate.SmoothSphereBivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate.SmoothSphereBivariateSpline.__init__(B is inconsistent, runtime argument "s" is not keyword-only
-scipy.interpolate.SmoothSphereBivariateSpline.__init__(B is inconsistent, runtime argument "eps" is not keyword-only
-scipy.interpolate.SphereBivariateSpline(B is not present at runtime
-scipy.interpolate.UnivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate.UnivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate.UnivariateSpline.__init__(B is inconsistent, runtime argument "k" is not keyword-only
-scipy.interpolate.UnivariateSpline.__init__(B is inconsistent, runtime argument "s" is not keyword-only
-scipy.interpolate.UnivariateSpline.__init__(B is inconsistent, runtime argument "ext" is not keyword-only
-scipy.interpolate.UnivariateSpline.__init__(B is inconsistent, runtime argument "check_finite" is not keyword-only
-scipy.interpolate.check_random_state(B is not present at runtime
-scipy.interpolate.cho_solve_banded(B is not present at runtime
-scipy.interpolate.cholesky_banded(B is not present at runtime
-scipy.interpolate.comb(B is not present at runtime
-scipy.interpolate.copy_if_needed(B is not present at runtime
-scipy.interpolate.csr_array(B is not present at runtime
-scipy.interpolate.dblint(B is not present at runtime
-scipy.interpolate.dfitpack_int(B is not present at runtime
-scipy.interpolate.err_mesg(B is not present at runtime
-scipy.interpolate.factorial(B is not present at runtime
-scipy.interpolate.float_factorial(B is not present at runtime
-scipy.interpolate.fpcheck(B is not present at runtime
-scipy.interpolate.get_lapack_funcs(B is not present at runtime
-scipy.interpolate.interpnd(B is not present in stub
-scipy.interpolate.minimize_scalar(B is not present at runtime
-scipy.interpolate.normalize_axis_index(B is not present at runtime
-scipy.interpolate.poch(B is not present at runtime
-scipy.interpolate.prepare_input(B is not present at runtime
-scipy.interpolate.solve(B is not present at runtime
-scipy.interpolate.solve_banded(B is not present at runtime
-scipy.interpolate._bsplines.solve(B is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-scipy.interpolate._cubic.solve(B is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-scipy.interpolate._fitpack2.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate._fitpack2.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate._fitpack2.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "k" is not keyword-only
-scipy.interpolate._fitpack2.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "ext" is not keyword-only
-scipy.interpolate._fitpack2.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime argument "check_finite" is not keyword-only
-scipy.interpolate._fitpack2.InterpolatedUnivariateSpline.__init__(B is inconsistent, runtime does not have argument "s"
-scipy.interpolate._fitpack2.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate._fitpack2.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate._fitpack2.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "kx" is not keyword-only
-scipy.interpolate._fitpack2.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "ky" is not keyword-only
-scipy.interpolate._fitpack2.LSQBivariateSpline.__init__(B is inconsistent, runtime argument "eps" is not keyword-only
-scipy.interpolate._fitpack2.LSQSphereBivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate._fitpack2.LSQSphereBivariateSpline.__init__(B is inconsistent, runtime argument "eps" is not keyword-only
-scipy.interpolate._fitpack2.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate._fitpack2.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate._fitpack2.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "k" is not keyword-only
-scipy.interpolate._fitpack2.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "ext" is not keyword-only
-scipy.interpolate._fitpack2.LSQUnivariateSpline.__init__(B is inconsistent, runtime argument "check_finite" is not keyword-only
-scipy.interpolate._fitpack2.RectBivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate._fitpack2.RectBivariateSpline.__init__(B is inconsistent, runtime argument "kx" is not keyword-only
-scipy.interpolate._fitpack2.RectBivariateSpline.__init__(B is inconsistent, runtime argument "ky" is not keyword-only
-scipy.interpolate._fitpack2.RectBivariateSpline.__init__(B is inconsistent, runtime argument "s" is not keyword-only
-scipy.interpolate._fitpack2.RectSphereBivariateSpline.__init__(B is inconsistent, runtime argument "pole_continuity" is not keyword-only
-scipy.interpolate._fitpack2.RectSphereBivariateSpline.__init__(B is inconsistent, runtime argument "pole_values" is not keyword-only
-scipy.interpolate._fitpack2.RectSphereBivariateSpline.__init__(B is inconsistent, runtime argument "pole_exact" is not keyword-only
-scipy.interpolate._fitpack2.RectSphereBivariateSpline.__init__(B is inconsistent, runtime argument "pole_flat" is not keyword-only
-scipy.interpolate._fitpack2.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate._fitpack2.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate._fitpack2.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "kx" is not keyword-only
-scipy.interpolate._fitpack2.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "ky" is not keyword-only
-scipy.interpolate._fitpack2.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "s" is not keyword-only
-scipy.interpolate._fitpack2.SmoothBivariateSpline.__init__(B is inconsistent, runtime argument "eps" is not keyword-only
-scipy.interpolate._fitpack2.SmoothSphereBivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate._fitpack2.SmoothSphereBivariateSpline.__init__(B is inconsistent, runtime argument "s" is not keyword-only
-scipy.interpolate._fitpack2.SmoothSphereBivariateSpline.__init__(B is inconsistent, runtime argument "eps" is not keyword-only
-scipy.interpolate._fitpack2.UnivariateSpline.__init__(B is inconsistent, runtime argument "w" is not keyword-only
-scipy.interpolate._fitpack2.UnivariateSpline.__init__(B is inconsistent, runtime argument "bbox" is not keyword-only
-scipy.interpolate._fitpack2.UnivariateSpline.__init__(B is inconsistent, runtime argument "k" is not keyword-only
-scipy.interpolate._fitpack2.UnivariateSpline.__init__(B is inconsistent, runtime argument "s" is not keyword-only
-scipy.interpolate._fitpack2.UnivariateSpline.__init__(B is inconsistent, runtime argument "ext" is not keyword-only
-scipy.interpolate._fitpack2.UnivariateSpline.__init__(B is inconsistent, runtime argument "check_finite" is not keyword-only
-scipy.interpolate._fitpack2._BivariateSplineBase.__call__(B is inconsistent, runtime argument "grid" is not keyword-only
-scipy.interpolate._interpolate.comb(B is inconsistent, stub argument "n" differs from runtime argument "N"
-scipy.interpolate._polyint.factorial(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.interpolate._rbfinterp_pythran.NAME_TO_FUNC(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.__pythran__(B is not present in stub
-scipy.interpolate._rbfinterp_pythran.cubic(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.gaussian(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.inverse_multiquadric(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.inverse_quadratic(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.kernel_matrix(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.kernel_vector(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.linear(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.multiquadric(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.polynomial_matrix(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.polynomial_vector(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.quintic(B is not present at runtime
-scipy.interpolate._rbfinterp_pythran.thin_plate_spline(B is not present at runtime
-scipy.interpolate.dfitpack.bispeu(B is not present in stub
-scipy.interpolate.dfitpack.bispev(B is not present in stub
-scipy.interpolate.dfitpack.curfit(B is not present in stub
-scipy.interpolate.dfitpack.dblint(B is not present in stub
-scipy.interpolate.dfitpack.fpchec(B is not present in stub
-scipy.interpolate.dfitpack.fpcurf0(B is not present in stub
-scipy.interpolate.dfitpack.fpcurf1(B is not present in stub
-scipy.interpolate.dfitpack.fpcurfm1(B is not present in stub
-scipy.interpolate.dfitpack.parcur(B is not present in stub
-scipy.interpolate.dfitpack.parder(B is not present in stub
-scipy.interpolate.dfitpack.pardeu(B is not present in stub
-scipy.interpolate.dfitpack.pardtc(B is not present in stub
-scipy.interpolate.dfitpack.percur(B is not present in stub
-scipy.interpolate.dfitpack.regrid_smth(B is not present in stub
-scipy.interpolate.dfitpack.regrid_smth_spher(B is not present in stub
-scipy.interpolate.dfitpack.spalde(B is not present in stub
-scipy.interpolate.dfitpack.spherfit_lsq(B is not present in stub
-scipy.interpolate.dfitpack.spherfit_smth(B is not present in stub
-scipy.interpolate.dfitpack.splder(B is not present in stub
-scipy.interpolate.dfitpack.splev(B is not present in stub
-scipy.interpolate.dfitpack.splint(B is not present in stub
-scipy.interpolate.dfitpack.sproot(B is not present in stub
-scipy.interpolate.dfitpack.surfit_lsq(B is not present in stub
-scipy.interpolate.dfitpack.surfit_smth(B is not present in stub
-scipy.interpolate.dfitpack.types(B is not present in stub
-scipy.interpolate.fitpack.BSpline(B is not present in stub
-scipy.interpolate.fitpack.bisplev(B is not present in stub
-scipy.interpolate.fitpack.bisplrep(B is not present in stub
-scipy.interpolate.fitpack.insert(B is not present in stub
-scipy.interpolate.fitpack.spalde(B is not present in stub
-scipy.interpolate.fitpack.splantider(B is not present in stub
-scipy.interpolate.fitpack.splder(B is not present in stub
-scipy.interpolate.fitpack.splev(B is not present in stub
-scipy.interpolate.fitpack.splint(B is not present in stub
-scipy.interpolate.fitpack.splprep(B is not present in stub
-scipy.interpolate.fitpack.splrep(B is not present in stub
-scipy.interpolate.fitpack.sproot(B is not present in stub
-scipy.interpolate.fitpack2.BivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.InterpolatedUnivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.LSQBivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.LSQSphereBivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.LSQUnivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.RectBivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.RectSphereBivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.SmoothBivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.SmoothSphereBivariateSpline(B is not present in stub
-scipy.interpolate.fitpack2.UnivariateSpline(B is not present in stub
-scipy.interpolate.interpnd.GradientEstimationWarning(B is not present in stub
-scipy.interpolate.interpnd.estimate_gradients_2d_global(B is not present in stub
-scipy.interpolate.interpolate.BPoly(B is not present in stub
-scipy.interpolate.interpolate.BSpline(B is not present in stub
-scipy.interpolate.interpolate.NdPPoly(B is not present in stub
-scipy.interpolate.interpolate.PPoly(B is not present in stub
-scipy.interpolate.interpolate.RectBivariateSpline(B is not present in stub
-scipy.interpolate.interpolate.RegularGridInterpolator(B is not present in stub
-scipy.interpolate.interpolate.interp1d(B is not present in stub
-scipy.interpolate.interpolate.interp2d(B is not present in stub
-scipy.interpolate.interpolate.interpn(B is not present in stub
-scipy.interpolate.interpolate.lagrange(B is not present in stub
-scipy.interpolate.interpolate.make_interp_spline(B is not present in stub
-scipy.interpolate.ndgriddata.CloughTocher2DInterpolator(B is not present in stub
-scipy.interpolate.ndgriddata.LinearNDInterpolator(B is not present in stub
-scipy.interpolate.ndgriddata.NearestNDInterpolator(B is not present in stub
-scipy.interpolate.ndgriddata.griddata(B is not present in stub
-scipy.interpolate.polyint.BarycentricInterpolator(B is not present in stub
-scipy.interpolate.polyint.KroghInterpolator(B is not present in stub
-scipy.interpolate.polyint.approximate_taylor_polynomial(B is not present in stub
-scipy.interpolate.polyint.barycentric_interpolate(B is not present in stub
-scipy.interpolate.polyint.krogh_interpolate(B is not present in stub
-scipy.interpolate.rbf.Rbf(B is not present in stub
-scipy.io.matlab(B is not present in stub
-scipy.io._fast_matrix_market._TextToBytesWrapper.peek(B is inconsistent, runtime argument "size" has a default value of -1, which is different from stub argument default 0
-scipy.io.arff.Attribute(B is not present at runtime
-scipy.io.arff.DateAttribute(B is not present at runtime
-scipy.io.arff.NominalAttribute(B is not present at runtime
-scipy.io.arff.NumericAttribute(B is not present at runtime
-scipy.io.arff.RelationalAttribute(B is not present at runtime
-scipy.io.arff.StringAttribute(B is not present at runtime
-scipy.io.arff.basic_stats(B is not present at runtime
-scipy.io.arff.csv_sniffer_has_bug_last_field(B is not present at runtime
-scipy.io.arff.print_attribute(B is not present at runtime
-scipy.io.arff.r_attribute(B is not present at runtime
-scipy.io.arff.r_comattrval(B is not present at runtime
-scipy.io.arff.r_comment(B is not present at runtime
-scipy.io.arff.r_datameta(B is not present at runtime
-scipy.io.arff.r_date(B is not present at runtime
-scipy.io.arff.r_empty(B is not present at runtime
-scipy.io.arff.r_headerline(B is not present at runtime
-scipy.io.arff.r_meta(B is not present at runtime
-scipy.io.arff.r_nominal(B is not present at runtime
-scipy.io.arff.r_relation(B is not present at runtime
-scipy.io.arff.r_wcomattrval(B is not present at runtime
-scipy.io.arff.read_header(B is not present at runtime
-scipy.io.arff.read_relational_attribute(B is not present at runtime
-scipy.io.arff.split_data_line(B is not present at runtime
-scipy.io.arff.to_attribute(B is not present at runtime
-scipy.io.arff.tokenize_attribute(B is not present at runtime
-scipy.io.arff.tokenize_single_comma(B is not present at runtime
-scipy.io.arff.tokenize_single_wcomma(B is not present at runtime
-scipy.io.arff.workaround_csv_sniffer_bug_last_field(B is not present at runtime
-scipy.io.arff._arffread.DateAttribute.type_name(B variable differs from runtime type None
-scipy.io.arff._arffread.NumericAttribute.type_name(B variable differs from runtime type None
-scipy.io.arff._arffread.RelationalAttribute.type_name(B variable differs from runtime type None
-scipy.io.arff._arffread.StringAttribute.type_name(B variable differs from runtime type None
-scipy.io.arff.arffread.ArffError(B is not present in stub
-scipy.io.arff.arffread.MetaData(B is not present in stub
-scipy.io.arff.arffread.ParseArffError(B is not present in stub
-scipy.io.arff.arffread.loadarff(B is not present in stub
-scipy.io.harwell_boeing.hb_read(B is not present in stub
-scipy.io.harwell_boeing.hb_write(B is not present in stub
-scipy.io.idl.readsav(B is not present in stub
-scipy.io.matlab.mio.loadmat(B is not present in stub
-scipy.io.matlab.mio.savemat(B is not present in stub
-scipy.io.matlab.mio.whosmat(B is not present in stub
-scipy.io.matlab.mio5.MatReadError(B is not present in stub
-scipy.io.matlab.mio5.MatReadWarning(B is not present in stub
-scipy.io.matlab.mio5.MatWriteError(B is not present in stub
-scipy.io.matlab.mio5.MatlabFunction(B is not present in stub
-scipy.io.matlab.mio5.MatlabObject(B is not present in stub
-scipy.io.matlab.mio5.mat_struct(B is not present in stub
-scipy.io.matlab.mio5.varmats_from_mat(B is not present in stub
-scipy.io.matlab.mio5_params.MatlabFunction(B is not present in stub
-scipy.io.matlab.mio5_params.MatlabObject(B is not present in stub
-scipy.io.matlab.mio5_params.MatlabOpaque(B is not present in stub
-scipy.io.matlab.mio5_params.mat_struct(B is not present in stub
-scipy.io.matlab.miobase.MatReadError(B is not present in stub
-scipy.io.matlab.miobase.MatReadWarning(B is not present in stub
-scipy.io.matlab.miobase.MatWriteError(B is not present in stub
-scipy.io.mmio.mminfo(B is not present in stub
-scipy.io.mmio.mmread(B is not present in stub
-scipy.io.mmio.mmwrite(B is not present in stub
-scipy.io.netcdf.netcdf_file(B is not present in stub
-scipy.io.netcdf.netcdf_variable(B is not present in stub
-scipy.linalg.cython_blas(B is not present in stub
-scipy.linalg.cython_lapack(B is not present in stub
-scipy.linalg.eig(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg.eig(B is inconsistent, stub argument "a" differs from runtime argument "b"
-scipy.linalg.eig(B is inconsistent, runtime argument "b" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.eig(B is inconsistent, stub argument "b" differs from runtime argument "left"
-scipy.linalg.eig(B is inconsistent, stub argument "b" differs from runtime argument "right"
-scipy.linalg.eig(B is inconsistent, stub argument "left" differs from runtime argument "overwrite_a"
-scipy.linalg.eig(B is inconsistent, stub argument "left" differs from runtime argument "overwrite_b"
-scipy.linalg.eig(B is inconsistent, stub argument "right" differs from runtime argument "check_finite"
-scipy.linalg.eig(B is inconsistent, stub argument "right" differs from runtime argument "homogeneous_eigvals"
-scipy.linalg.eig(B is inconsistent, runtime argument "homogeneous_eigvals" has a default value of type False, which is incompatible with stub argument type True. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.eig(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg.eig(B is inconsistent, runtime does not have argument "overwrite_b"
-scipy.linalg.eig(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.eig(B is inconsistent, runtime does not have argument "homogeneous_eigvals"
-scipy.linalg.eig_banded(B is inconsistent, stub argument "a_band" has a default value but runtime argument does not
-scipy.linalg.eig_banded(B is inconsistent, stub argument "a_band" differs from runtime argument "lower"
-scipy.linalg.eig_banded(B is inconsistent, stub argument "lower" differs from runtime argument "eigvals_only"
-scipy.linalg.eig_banded(B is inconsistent, stub argument "lower" differs from runtime argument "overwrite_a_band"
-scipy.linalg.eig_banded(B is inconsistent, stub argument "eigvals_only" differs from runtime argument "select"
-scipy.linalg.eig_banded(B is inconsistent, runtime argument "select" has a default value of type 'a', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.eig_banded(B is inconsistent, stub argument "eigvals_only" differs from runtime argument "select_range"
-scipy.linalg.eig_banded(B is inconsistent, runtime argument "select_range" has a default value of type None, which is incompatible with stub argument type True. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.eig_banded(B is inconsistent, stub argument "overwrite_a_band" differs from runtime argument "max_ev"
-scipy.linalg.eig_banded(B is inconsistent, stub argument "select" differs from runtime argument "check_finite"
-scipy.linalg.eig_banded(B is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'a' | 'v' | 'i'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.eig_banded(B is inconsistent, runtime does not have argument "select_range"
-scipy.linalg.eig_banded(B is inconsistent, runtime does not have argument "max_ev"
-scipy.linalg.eig_banded(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.eigvals_banded(B is inconsistent, runtime argument "lower" has a default value of False, which is different from stub argument default True
-scipy.linalg.expm_frechet(B is inconsistent, stub argument "A" has a default value but runtime argument does not
-scipy.linalg.expm_frechet(B is inconsistent, stub argument "A" differs from runtime argument "E"
-scipy.linalg.expm_frechet(B is inconsistent, stub argument "A" has a default value but runtime argument does not
-scipy.linalg.expm_frechet(B is inconsistent, stub argument "E" differs from runtime argument "method"
-scipy.linalg.expm_frechet(B is inconsistent, runtime argument "method" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.expm_frechet(B is inconsistent, stub argument "E" differs from runtime argument "compute_expm"
-scipy.linalg.expm_frechet(B is inconsistent, stub argument "method" differs from runtime argument "check_finite"
-scipy.linalg.expm_frechet(B is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'SPS' | 'blockEnlarge' | None. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.expm_frechet(B is inconsistent, runtime does not have argument "method"
-scipy.linalg.expm_frechet(B is inconsistent, runtime does not have argument "compute_expm"
-scipy.linalg.expm_frechet(B is inconsistent, runtime does not have argument "compute_expm"
-scipy.linalg.expm_frechet(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.invpascal(B is inconsistent, stub argument "n" has a default value but runtime argument does not
-scipy.linalg.invpascal(B is inconsistent, stub argument "n" differs from runtime argument "kind"
-scipy.linalg.invpascal(B is inconsistent, runtime argument "kind" has a default value of type 'symmetric', which is incompatible with stub argument type int | numpy.integer[numpy._typing.NBitBase] | numpy.bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.invpascal(B is inconsistent, stub argument "kind" differs from runtime argument "exact"
-scipy.linalg.invpascal(B is inconsistent, runtime argument "exact" has a default value of type True, which is incompatible with stub argument type 'symmetric' | 'upper' | 'lower'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.invpascal(B is inconsistent, runtime does not have argument "kind"
-scipy.linalg.invpascal(B is inconsistent, runtime does not have argument "exact"
-scipy.linalg.invpascal(B is inconsistent, runtime does not have argument "exact"
-scipy.linalg.lu(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg.lu(B is inconsistent, stub argument "a" differs from runtime argument "permute_l"
-scipy.linalg.lu(B is inconsistent, stub argument "permute_l" differs from runtime argument "overwrite_a"
-scipy.linalg.lu(B is inconsistent, stub argument "permute_l" differs from runtime argument "check_finite"
-scipy.linalg.lu(B is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.lu(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "p_indices"
-scipy.linalg.lu(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg.lu(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.lu(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.lu(B is inconsistent, runtime does not have argument "p_indices"
-scipy.linalg.lu(B is inconsistent, runtime does not have argument "p_indices"
-scipy.linalg.matmul_toeplitz(B is inconsistent, runtime argument "check_finite" has a default value of False, which is different from stub argument default True
-scipy.linalg.matrix_balance(B is inconsistent, stub argument "A" has a default value but runtime argument does not
-scipy.linalg.matrix_balance(B is inconsistent, stub argument "A" differs from runtime argument "permute"
-scipy.linalg.matrix_balance(B is inconsistent, stub argument "permute" differs from runtime argument "scale"
-scipy.linalg.matrix_balance(B is inconsistent, stub argument "permute" differs from runtime argument "separate"
-scipy.linalg.matrix_balance(B is inconsistent, stub argument "scale" differs from runtime argument "overwrite_a"
-scipy.linalg.matrix_balance(B is inconsistent, runtime does not have argument "scale"
-scipy.linalg.matrix_balance(B is inconsistent, runtime does not have argument "separate"
-scipy.linalg.matrix_balance(B is inconsistent, runtime does not have argument "separate"
-scipy.linalg.matrix_balance(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg.null_space(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.null_space(B is inconsistent, runtime does not have argument "lapack_driver"
-scipy.linalg.null_space(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg.pascal(B is inconsistent, stub argument "n" has a default value but runtime argument does not
-scipy.linalg.pascal(B is inconsistent, stub argument "n" differs from runtime argument "kind"
-scipy.linalg.pascal(B is inconsistent, runtime argument "kind" has a default value of type 'symmetric', which is incompatible with stub argument type int | numpy.integer[numpy._typing.NBitBase] | numpy.bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.pascal(B is inconsistent, stub argument "kind" differs from runtime argument "exact"
-scipy.linalg.pascal(B is inconsistent, runtime argument "exact" has a default value of type True, which is incompatible with stub argument type 'symmetric' | 'upper' | 'lower'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.pascal(B is inconsistent, runtime does not have argument "kind"
-scipy.linalg.pascal(B is inconsistent, runtime does not have argument "exact"
-scipy.linalg.pascal(B is inconsistent, runtime does not have argument "exact"
-scipy.linalg.pinvh(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg.pinvh(B is inconsistent, stub argument "a" differs from runtime argument "atol"
-scipy.linalg.pinvh(B is inconsistent, runtime argument "atol" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.pinvh(B is inconsistent, stub argument "atol" differs from runtime argument "rtol"
-scipy.linalg.pinvh(B is inconsistent, stub argument "atol" differs from runtime argument "lower"
-scipy.linalg.pinvh(B is inconsistent, stub argument "rtol" differs from runtime argument "return_rank"
-scipy.linalg.pinvh(B is inconsistent, stub argument "rtol" differs from runtime argument "check_finite"
-scipy.linalg.pinvh(B is inconsistent, runtime does not have argument "lower"
-scipy.linalg.pinvh(B is inconsistent, runtime does not have argument "lower"
-scipy.linalg.pinvh(B is inconsistent, runtime does not have argument "return_rank"
-scipy.linalg.pinvh(B is inconsistent, runtime does not have argument "return_rank"
-scipy.linalg.pinvh(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.qr(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg.qr(B is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-scipy.linalg.qr(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-scipy.linalg.qr(B is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.qr(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-scipy.linalg.qr(B is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.qr(B is inconsistent, stub argument "lwork" differs from runtime argument "pivoting"
-scipy.linalg.qr(B is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-scipy.linalg.qr(B is inconsistent, runtime does not have argument "mode"
-scipy.linalg.qr(B is inconsistent, runtime does not have argument "mode"
-scipy.linalg.qr(B is inconsistent, runtime does not have argument "pivoting"
-scipy.linalg.qr(B is inconsistent, runtime does not have argument "pivoting"
-scipy.linalg.qr(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.qr_multiply(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg.qr_multiply(B is inconsistent, stub argument "a" differs from runtime argument "c"
-scipy.linalg.qr_multiply(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg.qr_multiply(B is inconsistent, stub argument "c" differs from runtime argument "mode"
-scipy.linalg.qr_multiply(B is inconsistent, stub argument "c" differs from runtime argument "pivoting"
-scipy.linalg.qr_multiply(B is inconsistent, stub argument "mode" differs from runtime argument "conjugate"
-scipy.linalg.qr_multiply(B is inconsistent, runtime argument "conjugate" has a default value of type False, which is incompatible with stub argument type 'left' | 'right'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.qr_multiply(B is inconsistent, stub argument "mode" differs from runtime argument "overwrite_a"
-scipy.linalg.qr_multiply(B is inconsistent, runtime argument "overwrite_a" has a default value of type False, which is incompatible with stub argument type 'left' | 'right'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.qr_multiply(B is inconsistent, stub argument "pivoting" differs from runtime argument "overwrite_c"
-scipy.linalg.qr_multiply(B is inconsistent, runtime does not have argument "pivoting"
-scipy.linalg.qr_multiply(B is inconsistent, runtime does not have argument "conjugate"
-scipy.linalg.qr_multiply(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg.qr_multiply(B is inconsistent, runtime does not have argument "overwrite_c"
-scipy.linalg.rq(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg.rq(B is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-scipy.linalg.rq(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-scipy.linalg.rq(B is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.rq(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-scipy.linalg.rq(B is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.rq(B is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-scipy.linalg.rq(B is inconsistent, runtime does not have argument "lwork"
-scipy.linalg.rq(B is inconsistent, runtime does not have argument "mode"
-scipy.linalg.rq(B is inconsistent, runtime does not have argument "mode"
-scipy.linalg.rq(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.solve(B is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-scipy.linalg.svd(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg.svd(B is inconsistent, stub argument "a" differs from runtime argument "full_matrices"
-scipy.linalg.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "compute_uv"
-scipy.linalg.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "overwrite_a"
-scipy.linalg.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "check_finite"
-scipy.linalg.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "lapack_driver"
-scipy.linalg.svd(B is inconsistent, runtime argument "lapack_driver" has a default value of type 'gesdd', which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg.svd(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg.svd(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg.svd(B is inconsistent, runtime does not have argument "lapack_driver"
-scipy.linalg._basic.matmul_toeplitz(B is inconsistent, runtime argument "check_finite" has a default value of False, which is different from stub argument default True
-scipy.linalg._basic.matrix_balance(B is inconsistent, stub argument "A" has a default value but runtime argument does not
-scipy.linalg._basic.matrix_balance(B is inconsistent, stub argument "A" differs from runtime argument "permute"
-scipy.linalg._basic.matrix_balance(B is inconsistent, stub argument "permute" differs from runtime argument "scale"
-scipy.linalg._basic.matrix_balance(B is inconsistent, stub argument "permute" differs from runtime argument "separate"
-scipy.linalg._basic.matrix_balance(B is inconsistent, stub argument "scale" differs from runtime argument "overwrite_a"
-scipy.linalg._basic.matrix_balance(B is inconsistent, runtime does not have argument "scale"
-scipy.linalg._basic.matrix_balance(B is inconsistent, runtime does not have argument "separate"
-scipy.linalg._basic.matrix_balance(B is inconsistent, runtime does not have argument "separate"
-scipy.linalg._basic.matrix_balance(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg._basic.pinvh(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg._basic.pinvh(B is inconsistent, stub argument "a" differs from runtime argument "atol"
-scipy.linalg._basic.pinvh(B is inconsistent, runtime argument "atol" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._basic.pinvh(B is inconsistent, stub argument "atol" differs from runtime argument "rtol"
-scipy.linalg._basic.pinvh(B is inconsistent, stub argument "atol" differs from runtime argument "lower"
-scipy.linalg._basic.pinvh(B is inconsistent, stub argument "rtol" differs from runtime argument "return_rank"
-scipy.linalg._basic.pinvh(B is inconsistent, stub argument "rtol" differs from runtime argument "check_finite"
-scipy.linalg._basic.pinvh(B is inconsistent, runtime does not have argument "lower"
-scipy.linalg._basic.pinvh(B is inconsistent, runtime does not have argument "lower"
-scipy.linalg._basic.pinvh(B is inconsistent, runtime does not have argument "return_rank"
-scipy.linalg._basic.pinvh(B is inconsistent, runtime does not have argument "return_rank"
-scipy.linalg._basic.pinvh(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._basic.solve(B is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-scipy.linalg._cythonized_array_utils.numeric_t(B is not present at runtime
-scipy.linalg._decomp.eig(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg._decomp.eig(B is inconsistent, stub argument "a" differs from runtime argument "b"
-scipy.linalg._decomp.eig(B is inconsistent, runtime argument "b" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp.eig(B is inconsistent, stub argument "b" differs from runtime argument "left"
-scipy.linalg._decomp.eig(B is inconsistent, stub argument "b" differs from runtime argument "right"
-scipy.linalg._decomp.eig(B is inconsistent, stub argument "left" differs from runtime argument "overwrite_a"
-scipy.linalg._decomp.eig(B is inconsistent, stub argument "left" differs from runtime argument "overwrite_b"
-scipy.linalg._decomp.eig(B is inconsistent, stub argument "right" differs from runtime argument "check_finite"
-scipy.linalg._decomp.eig(B is inconsistent, stub argument "right" differs from runtime argument "homogeneous_eigvals"
-scipy.linalg._decomp.eig(B is inconsistent, runtime argument "homogeneous_eigvals" has a default value of type False, which is incompatible with stub argument type True. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp.eig(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg._decomp.eig(B is inconsistent, runtime does not have argument "overwrite_b"
-scipy.linalg._decomp.eig(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._decomp.eig(B is inconsistent, runtime does not have argument "homogeneous_eigvals"
-scipy.linalg._decomp.eig_banded(B is inconsistent, stub argument "a_band" has a default value but runtime argument does not
-scipy.linalg._decomp.eig_banded(B is inconsistent, stub argument "a_band" differs from runtime argument "lower"
-scipy.linalg._decomp.eig_banded(B is inconsistent, stub argument "lower" differs from runtime argument "eigvals_only"
-scipy.linalg._decomp.eig_banded(B is inconsistent, stub argument "lower" differs from runtime argument "overwrite_a_band"
-scipy.linalg._decomp.eig_banded(B is inconsistent, stub argument "eigvals_only" differs from runtime argument "select"
-scipy.linalg._decomp.eig_banded(B is inconsistent, runtime argument "select" has a default value of type 'a', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp.eig_banded(B is inconsistent, stub argument "eigvals_only" differs from runtime argument "select_range"
-scipy.linalg._decomp.eig_banded(B is inconsistent, runtime argument "select_range" has a default value of type None, which is incompatible with stub argument type True. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp.eig_banded(B is inconsistent, stub argument "overwrite_a_band" differs from runtime argument "max_ev"
-scipy.linalg._decomp.eig_banded(B is inconsistent, stub argument "select" differs from runtime argument "check_finite"
-scipy.linalg._decomp.eig_banded(B is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'a' | 'v' | 'i'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp.eig_banded(B is inconsistent, runtime does not have argument "select_range"
-scipy.linalg._decomp.eig_banded(B is inconsistent, runtime does not have argument "max_ev"
-scipy.linalg._decomp.eig_banded(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._decomp.eigvals_banded(B is inconsistent, runtime argument "lower" has a default value of False, which is different from stub argument default True
-scipy.linalg._decomp_lu.lu(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg._decomp_lu.lu(B is inconsistent, stub argument "a" differs from runtime argument "permute_l"
-scipy.linalg._decomp_lu.lu(B is inconsistent, stub argument "permute_l" differs from runtime argument "overwrite_a"
-scipy.linalg._decomp_lu.lu(B is inconsistent, stub argument "permute_l" differs from runtime argument "check_finite"
-scipy.linalg._decomp_lu.lu(B is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp_lu.lu(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "p_indices"
-scipy.linalg._decomp_lu.lu(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg._decomp_lu.lu(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._decomp_lu.lu(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._decomp_lu.lu(B is inconsistent, runtime does not have argument "p_indices"
-scipy.linalg._decomp_lu.lu(B is inconsistent, runtime does not have argument "p_indices"
-scipy.linalg._decomp_lu_cython.lu_decompose(B is not present at runtime
-scipy.linalg._decomp_lu_cython.lu_dispatcher(B is inconsistent, stub argument "lu" differs from runtime argument "u"
-scipy.linalg._decomp_lu_cython.lu_dispatcher(B is inconsistent, stub argument "perm" differs from runtime argument "piv"
-scipy.linalg._decomp_qr.qr(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg._decomp_qr.qr(B is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-scipy.linalg._decomp_qr.qr(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-scipy.linalg._decomp_qr.qr(B is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp_qr.qr(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-scipy.linalg._decomp_qr.qr(B is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp_qr.qr(B is inconsistent, stub argument "lwork" differs from runtime argument "pivoting"
-scipy.linalg._decomp_qr.qr(B is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-scipy.linalg._decomp_qr.qr(B is inconsistent, runtime does not have argument "mode"
-scipy.linalg._decomp_qr.qr(B is inconsistent, runtime does not have argument "mode"
-scipy.linalg._decomp_qr.qr(B is inconsistent, runtime does not have argument "pivoting"
-scipy.linalg._decomp_qr.qr(B is inconsistent, runtime does not have argument "pivoting"
-scipy.linalg._decomp_qr.qr(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, stub argument "a" differs from runtime argument "c"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, stub argument "c" differs from runtime argument "mode"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, stub argument "c" differs from runtime argument "pivoting"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, stub argument "mode" differs from runtime argument "conjugate"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, runtime argument "conjugate" has a default value of type False, which is incompatible with stub argument type 'left' | 'right'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, stub argument "mode" differs from runtime argument "overwrite_a"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, runtime argument "overwrite_a" has a default value of type False, which is incompatible with stub argument type 'left' | 'right'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, stub argument "pivoting" differs from runtime argument "overwrite_c"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, runtime does not have argument "pivoting"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, runtime does not have argument "conjugate"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg._decomp_qr.qr_multiply(B is inconsistent, runtime does not have argument "overwrite_c"
-scipy.linalg._decomp_qr.rq(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg._decomp_qr.rq(B is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-scipy.linalg._decomp_qr.rq(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-scipy.linalg._decomp_qr.rq(B is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp_qr.rq(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-scipy.linalg._decomp_qr.rq(B is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp_qr.rq(B is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-scipy.linalg._decomp_qr.rq(B is inconsistent, runtime does not have argument "lwork"
-scipy.linalg._decomp_qr.rq(B is inconsistent, runtime does not have argument "mode"
-scipy.linalg._decomp_qr.rq(B is inconsistent, runtime does not have argument "mode"
-scipy.linalg._decomp_qr.rq(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._decomp_svd.null_space(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._decomp_svd.null_space(B is inconsistent, runtime does not have argument "lapack_driver"
-scipy.linalg._decomp_svd.null_space(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg._decomp_svd.svd(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.linalg._decomp_svd.svd(B is inconsistent, stub argument "a" differs from runtime argument "full_matrices"
-scipy.linalg._decomp_svd.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "compute_uv"
-scipy.linalg._decomp_svd.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "overwrite_a"
-scipy.linalg._decomp_svd.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "check_finite"
-scipy.linalg._decomp_svd.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "lapack_driver"
-scipy.linalg._decomp_svd.svd(B is inconsistent, runtime argument "lapack_driver" has a default value of type 'gesdd', which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._decomp_svd.svd(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.linalg._decomp_svd.svd(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._decomp_svd.svd(B is inconsistent, runtime does not have argument "lapack_driver"
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, stub argument "A" has a default value but runtime argument does not
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, stub argument "A" differs from runtime argument "E"
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, stub argument "A" has a default value but runtime argument does not
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, stub argument "E" differs from runtime argument "method"
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, runtime argument "method" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, stub argument "E" differs from runtime argument "compute_expm"
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, stub argument "method" differs from runtime argument "check_finite"
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'SPS' | 'blockEnlarge' | None. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, runtime does not have argument "method"
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, runtime does not have argument "compute_expm"
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, runtime does not have argument "compute_expm"
-scipy.linalg._expm_frechet.expm_frechet(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, stub argument "A" has a default value but runtime argument does not
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, stub argument "A" differs from runtime argument "E"
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, stub argument "A" has a default value but runtime argument does not
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, stub argument "E" differs from runtime argument "method"
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, runtime argument "method" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, stub argument "E" differs from runtime argument "compute_expm"
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, stub argument "method" differs from runtime argument "check_finite"
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, runtime argument "check_finite" has a default value of type True, which is incompatible with stub argument type 'SPS' | 'blockEnlarge' | None. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, runtime does not have argument "method"
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, runtime does not have argument "compute_expm"
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, runtime does not have argument "compute_expm"
-scipy.linalg._matfuncs.expm_frechet(B is inconsistent, runtime does not have argument "check_finite"
-scipy.linalg._matfuncs_expm.pick_pade_structure(B is inconsistent, stub argument "a" differs from runtime argument "Am"
-scipy.linalg._special_matrices.invpascal(B is inconsistent, stub argument "n" has a default value but runtime argument does not
-scipy.linalg._special_matrices.invpascal(B is inconsistent, stub argument "n" differs from runtime argument "kind"
-scipy.linalg._special_matrices.invpascal(B is inconsistent, runtime argument "kind" has a default value of type 'symmetric', which is incompatible with stub argument type int | numpy.integer[numpy._typing.NBitBase] | numpy.bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._special_matrices.invpascal(B is inconsistent, stub argument "kind" differs from runtime argument "exact"
-scipy.linalg._special_matrices.invpascal(B is inconsistent, runtime argument "exact" has a default value of type True, which is incompatible with stub argument type 'symmetric' | 'upper' | 'lower'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._special_matrices.invpascal(B is inconsistent, runtime does not have argument "kind"
-scipy.linalg._special_matrices.invpascal(B is inconsistent, runtime does not have argument "exact"
-scipy.linalg._special_matrices.invpascal(B is inconsistent, runtime does not have argument "exact"
-scipy.linalg._special_matrices.pascal(B is inconsistent, stub argument "n" has a default value but runtime argument does not
-scipy.linalg._special_matrices.pascal(B is inconsistent, stub argument "n" differs from runtime argument "kind"
-scipy.linalg._special_matrices.pascal(B is inconsistent, runtime argument "kind" has a default value of type 'symmetric', which is incompatible with stub argument type int | numpy.integer[numpy._typing.NBitBase] | numpy.bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._special_matrices.pascal(B is inconsistent, stub argument "kind" differs from runtime argument "exact"
-scipy.linalg._special_matrices.pascal(B is inconsistent, runtime argument "exact" has a default value of type True, which is incompatible with stub argument type 'symmetric' | 'upper' | 'lower'. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.linalg._special_matrices.pascal(B is inconsistent, runtime does not have argument "kind"
-scipy.linalg._special_matrices.pascal(B is inconsistent, runtime does not have argument "exact"
-scipy.linalg._special_matrices.pascal(B is inconsistent, runtime does not have argument "exact"
-scipy.linalg.interpolative.estimate_rank(B is inconsistent, runtime does not have argument "rng"
-scipy.linalg.interpolative.estimate_spectral_norm(B is inconsistent, runtime does not have argument "rng"
-scipy.linalg.interpolative.estimate_spectral_norm_diff(B is inconsistent, runtime does not have argument "rng"
-scipy.linalg.interpolative.interp_decomp(B is inconsistent, runtime does not have argument "rng"
-scipy.linalg.interpolative.seed(B is inconsistent, runtime argument "seed" has a default value of type None, which is incompatible with stub argument type Never
-scipy.linalg.interpolative.svd(B is inconsistent, runtime does not have argument "rng"
-scipy.misc.common.ascent(B is not present in stub
-scipy.misc.common.central_diff_weights(B is not present in stub
-scipy.misc.common.derivative(B is not present in stub
-scipy.misc.common.electrocardiogram(B is not present in stub
-scipy.misc.common.face(B is not present in stub
-scipy.misc.doccer.docformat(B is not present in stub
-scipy.misc.doccer.extend_notes_in_docstring(B is not present in stub
-scipy.misc.doccer.filldoc(B is not present in stub
-scipy.misc.doccer.indentcount_lines(B is not present in stub
-scipy.misc.doccer.inherit_docstring_from(B is not present in stub
-scipy.misc.doccer.replace_notes_in_docstring(B is not present in stub
-scipy.misc.doccer.unindent_dict(B is not present in stub
-scipy.misc.doccer.unindent_string(B is not present in stub
-scipy.ndimage.docfiller(B is not present at runtime
-scipy.ndimage.geometric_transform(B is inconsistent, runtime argument "extra_keywords" has a default value of {}, which is different from stub argument default None
-scipy.ndimage.normalize_axis_index(B is not present at runtime
-scipy.ndimage._dispatchers(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.ndimage._dispatchers'
-scipy.ndimage._interpolation.geometric_transform(B is inconsistent, runtime argument "extra_keywords" has a default value of {}, which is different from stub argument default None
-scipy.ndimage._ndimage_api(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.ndimage._ndimage_api'
-scipy.ndimage.filters.convolve(B is not present in stub
-scipy.ndimage.filters.convolve1d(B is not present in stub
-scipy.ndimage.filters.correlate(B is not present in stub
-scipy.ndimage.filters.correlate1d(B is not present in stub
-scipy.ndimage.filters.gaussian_filter(B is not present in stub
-scipy.ndimage.filters.gaussian_filter1d(B is not present in stub
-scipy.ndimage.filters.gaussian_gradient_magnitude(B is not present in stub
-scipy.ndimage.filters.gaussian_laplace(B is not present in stub
-scipy.ndimage.filters.generic_filter(B is not present in stub
-scipy.ndimage.filters.generic_filter1d(B is not present in stub
-scipy.ndimage.filters.generic_gradient_magnitude(B is not present in stub
-scipy.ndimage.filters.generic_laplace(B is not present in stub
-scipy.ndimage.filters.laplace(B is not present in stub
-scipy.ndimage.filters.maximum_filter(B is not present in stub
-scipy.ndimage.filters.maximum_filter1d(B is not present in stub
-scipy.ndimage.filters.median_filter(B is not present in stub
-scipy.ndimage.filters.minimum_filter(B is not present in stub
-scipy.ndimage.filters.minimum_filter1d(B is not present in stub
-scipy.ndimage.filters.percentile_filter(B is not present in stub
-scipy.ndimage.filters.prewitt(B is not present in stub
-scipy.ndimage.filters.rank_filter(B is not present in stub
-scipy.ndimage.filters.sobel(B is not present in stub
-scipy.ndimage.filters.uniform_filter(B is not present in stub
-scipy.ndimage.filters.uniform_filter1d(B is not present in stub
-scipy.ndimage.fourier.fourier_ellipsoid(B is not present in stub
-scipy.ndimage.fourier.fourier_gaussian(B is not present in stub
-scipy.ndimage.fourier.fourier_shift(B is not present in stub
-scipy.ndimage.fourier.fourier_uniform(B is not present in stub
-scipy.ndimage.interpolation.affine_transform(B is not present in stub
-scipy.ndimage.interpolation.geometric_transform(B is not present in stub
-scipy.ndimage.interpolation.map_coordinates(B is not present in stub
-scipy.ndimage.interpolation.rotate(B is not present in stub
-scipy.ndimage.interpolation.shift(B is not present in stub
-scipy.ndimage.interpolation.spline_filter(B is not present in stub
-scipy.ndimage.interpolation.spline_filter1d(B is not present in stub
-scipy.ndimage.interpolation.zoom(B is not present in stub
-scipy.ndimage.measurements.center_of_mass(B is not present in stub
-scipy.ndimage.measurements.extrema(B is not present in stub
-scipy.ndimage.measurements.find_objects(B is not present in stub
-scipy.ndimage.measurements.histogram(B is not present in stub
-scipy.ndimage.measurements.label(B is not present in stub
-scipy.ndimage.measurements.labeled_comprehension(B is not present in stub
-scipy.ndimage.measurements.maximum(B is not present in stub
-scipy.ndimage.measurements.maximum_position(B is not present in stub
-scipy.ndimage.measurements.mean(B is not present in stub
-scipy.ndimage.measurements.median(B is not present in stub
-scipy.ndimage.measurements.minimum(B is not present in stub
-scipy.ndimage.measurements.minimum_position(B is not present in stub
-scipy.ndimage.measurements.standard_deviation(B is not present in stub
-scipy.ndimage.measurements.sum(B is not present in stub
-scipy.ndimage.measurements.sum_labels(B is not present in stub
-scipy.ndimage.measurements.variance(B is not present in stub
-scipy.ndimage.measurements.watershed_ift(B is not present in stub
-scipy.ndimage.morphology.binary_closing(B is not present in stub
-scipy.ndimage.morphology.binary_dilation(B is not present in stub
-scipy.ndimage.morphology.binary_erosion(B is not present in stub
-scipy.ndimage.morphology.binary_fill_holes(B is not present in stub
-scipy.ndimage.morphology.binary_hit_or_miss(B is not present in stub
-scipy.ndimage.morphology.binary_opening(B is not present in stub
-scipy.ndimage.morphology.binary_propagation(B is not present in stub
-scipy.ndimage.morphology.black_tophat(B is not present in stub
-scipy.ndimage.morphology.distance_transform_bf(B is not present in stub
-scipy.ndimage.morphology.distance_transform_cdt(B is not present in stub
-scipy.ndimage.morphology.distance_transform_edt(B is not present in stub
-scipy.ndimage.morphology.generate_binary_structure(B is not present in stub
-scipy.ndimage.morphology.grey_closing(B is not present in stub
-scipy.ndimage.morphology.grey_dilation(B is not present in stub
-scipy.ndimage.morphology.grey_erosion(B is not present in stub
-scipy.ndimage.morphology.grey_opening(B is not present in stub
-scipy.ndimage.morphology.iterate_structure(B is not present in stub
-scipy.ndimage.morphology.morphological_gradient(B is not present in stub
-scipy.ndimage.morphology.morphological_laplace(B is not present in stub
-scipy.ndimage.morphology.white_tophat(B is not present in stub
-scipy.odr.models.Model(B is not present in stub
-scipy.odr.models.exponential(B is not present in stub
-scipy.odr.models.multilinear(B is not present in stub
-scipy.odr.models.polynomial(B is not present in stub
-scipy.odr.models.quadratic(B is not present in stub
-scipy.odr.models.unilinear(B is not present in stub
-scipy.odr.odrpack.Data(B is not present in stub
-scipy.odr.odrpack.Model(B is not present in stub
-scipy.odr.odrpack.ODR(B is not present in stub
-scipy.odr.odrpack.OdrError(B is not present in stub
-scipy.odr.odrpack.OdrStop(B is not present in stub
-scipy.odr.odrpack.OdrWarning(B is not present in stub
-scipy.odr.odrpack.Output(B is not present in stub
-scipy.odr.odrpack.RealData(B is not present in stub
-scipy.odr.odrpack.odr(B is not present in stub
-scipy.odr.odrpack.odr_error(B is not present in stub
-scipy.odr.odrpack.odr_stop(B is not present in stub
-scipy.optimize.KrylovJacobian.setup(B is inconsistent, stub argument "F" differs from runtime argument "f"
-scipy.optimize.KrylovJacobian.update(B is inconsistent, stub argument "F" differs from runtime argument "f"
-scipy.optimize.NonlinearConstraint.__init__(B is inconsistent, runtime argument "hess" has a default value of <scipy.optimize._hessian_update_strategy.BFGS object at 0x7284d477d9f0>, which is different from stub argument default None
-scipy.optimize._constraints.NonlinearConstraint.__init__(B is inconsistent, runtime argument "hess" has a default value of <scipy.optimize._hessian_update_strategy.BFGS object at 0x7284d477d9f0>, which is different from stub argument default None
-scipy.optimize._differentialevolution.NonlinearConstraint.__init__(B is inconsistent, runtime argument "hess" has a default value of <scipy.optimize._hessian_update_strategy.BFGS object at 0x7284d477d9f0>, which is different from stub argument default None
-scipy.optimize._elementwise(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.optimize._elementwise'
-scipy.optimize._group_columns.__pythran__(B is not present in stub
-scipy.optimize._highs.cython(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.optimize._highs.cython'
-scipy.optimize._highs.cython.src(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.optimize._highs.cython'
-scipy.optimize._lbfgsb_py.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.optimize._lbfgsb_py.LinearOperator.dtype(B is not present at runtime
-scipy.optimize._linprog_util._LPProblem.__new__(B is inconsistent, runtime argument "A_ub" has a default value but stub argument does not
-scipy.optimize._linprog_util._LPProblem.__new__(B is inconsistent, runtime argument "b_ub" has a default value but stub argument does not
-scipy.optimize._linprog_util._LPProblem.__new__(B is inconsistent, runtime argument "A_eq" has a default value but stub argument does not
-scipy.optimize._linprog_util._LPProblem.__new__(B is inconsistent, runtime argument "b_eq" has a default value but stub argument does not
-scipy.optimize._linprog_util._LPProblem.__new__(B is inconsistent, runtime argument "bounds" has a default value but stub argument does not
-scipy.optimize._linprog_util._LPProblem.__new__(B is inconsistent, runtime argument "x0" has a default value but stub argument does not
-scipy.optimize._linprog_util._LPProblem.__new__(B is inconsistent, runtime argument "integrality" has a default value but stub argument does not
-scipy.optimize._lsq.least_squares(B is inconsistent, runtime argument "tr_options" has a default value of {}, which is different from stub argument default None
-scipy.optimize._lsq.least_squares(B is inconsistent, runtime argument "kwargs" has a default value of {}, which is different from stub argument default None
-scipy.optimize._lsq.common.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.optimize._lsq.common.LinearOperator.dtype(B is not present at runtime
-scipy.optimize._lsq.dogbox.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.optimize._lsq.dogbox.LinearOperator.dtype(B is not present at runtime
-scipy.optimize._lsq.least_squares.least_squares(B is inconsistent, runtime argument "tr_options" has a default value of {}, which is different from stub argument default None
-scipy.optimize._lsq.least_squares.least_squares(B is inconsistent, runtime argument "kwargs" has a default value of {}, which is different from stub argument default None
-scipy.optimize._lsq.trf.qr(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.optimize._lsq.trf.qr(B is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-scipy.optimize._lsq.trf.qr(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-scipy.optimize._lsq.trf.qr(B is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.optimize._lsq.trf.qr(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-scipy.optimize._lsq.trf.qr(B is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.optimize._lsq.trf.qr(B is inconsistent, stub argument "lwork" differs from runtime argument "pivoting"
-scipy.optimize._lsq.trf.qr(B is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-scipy.optimize._lsq.trf.qr(B is inconsistent, runtime does not have argument "mode"
-scipy.optimize._lsq.trf.qr(B is inconsistent, runtime does not have argument "mode"
-scipy.optimize._lsq.trf.qr(B is inconsistent, runtime does not have argument "pivoting"
-scipy.optimize._lsq.trf.qr(B is inconsistent, runtime does not have argument "pivoting"
-scipy.optimize._lsq.trf.qr(B is inconsistent, runtime does not have argument "check_finite"
-scipy.optimize._lsq.trf.svd(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.optimize._lsq.trf.svd(B is inconsistent, stub argument "a" differs from runtime argument "full_matrices"
-scipy.optimize._lsq.trf.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "compute_uv"
-scipy.optimize._lsq.trf.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "overwrite_a"
-scipy.optimize._lsq.trf.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "check_finite"
-scipy.optimize._lsq.trf.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "lapack_driver"
-scipy.optimize._lsq.trf.svd(B is inconsistent, runtime argument "lapack_driver" has a default value of type 'gesdd', which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.optimize._lsq.trf.svd(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.optimize._lsq.trf.svd(B is inconsistent, runtime does not have argument "check_finite"
-scipy.optimize._lsq.trf.svd(B is inconsistent, runtime does not have argument "lapack_driver"
-scipy.optimize._nonlin.GenericBroyden.setup(B is inconsistent, stub argument "x" differs from runtime argument "x0"
-scipy.optimize._nonlin.GenericBroyden.setup(B is inconsistent, stub argument "F" differs from runtime argument "f0"
-scipy.optimize._nonlin.GenericBroyden.update(B is inconsistent, stub argument "F" differs from runtime argument "f"
-scipy.optimize._nonlin.Jacobian.dtype(B is not present at runtime
-scipy.optimize._nonlin.Jacobian.shape(B is not present at runtime
-scipy.optimize._nonlin.KrylovJacobian.setup(B is inconsistent, stub argument "F" differs from runtime argument "f"
-scipy.optimize._nonlin.KrylovJacobian.update(B is inconsistent, stub argument "F" differs from runtime argument "f"
-scipy.optimize._numdiff.approx_derivative(B is inconsistent, runtime argument "kwargs" has a default value of {}, which is different from stub argument default None
-scipy.optimize._numdiff.check_derivative(B is inconsistent, runtime argument "kwargs" has a default value of {}, which is different from stub argument default None
-scipy.optimize._trustregion_constr._minimize_trustregion_constr(B is not present in stub
-scipy.optimize._trustregion_constr.minimize_trustregion_constr.LagrangianHessian.__call__(B is inconsistent, stub argument "v_ineq" has a default value but runtime argument does not
-scipy.optimize._trustregion_constr.minimize_trustregion_constr.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.optimize._trustregion_constr.minimize_trustregion_constr.LinearOperator.dtype(B is not present at runtime
-scipy.optimize._trustregion_constr.minimize_trustregion_constr.NonlinearConstraint.__init__(B is inconsistent, runtime argument "hess" has a default value of <scipy.optimize._hessian_update_strategy.BFGS object at 0x7284d477d9f0>, which is different from stub argument default None
-scipy.optimize._trustregion_constr.projections.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.optimize._trustregion_constr.projections.LinearOperator.dtype(B is not present at runtime
-scipy.optimize._trustregion_constr.tr_interior_point.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.optimize._trustregion_constr.tr_interior_point.LinearOperator.dtype(B is not present at runtime
-scipy.optimize._trustregion_exact._minimize_trustregion_exact(B is not present in stub
-scipy.optimize._trustregion_krylov._minimize_trust_krylov(B is not present in stub
-scipy.signal.BSpline(B is not present at runtime
-scipy.signal.EPSILON(B is not present at runtime
-scipy.signal.LinAlgError(B is not present at runtime
-scipy.signal.LinAlgWarning(B is not present at runtime
-scipy.signal.abs(B is not present at runtime
-scipy.signal.axis_reverse(B is not present at runtime
-scipy.signal.axis_slice(B is not present at runtime
-scipy.signal.band_dict(B is not present at runtime
-scipy.signal.bessel_norms(B is not present at runtime
-scipy.signal.cascade(B is not present in stub
-scipy.signal.cmplx_sort(B is not present in stub
-scipy.signal.collapse_2d(B is not present at runtime
-scipy.signal.comb(B is not present at runtime
-scipy.signal.compute_root_from_lambda(B is not present at runtime
-scipy.signal.const_ext(B is not present at runtime
-scipy.signal.cwt(B is not present in stub
-scipy.signal.daub(B is not present in stub
-scipy.signal.even_ext(B is not present at runtime
-scipy.signal.fft(B is not present at runtime
-scipy.signal.filter_dict(B is not present at runtime
-scipy.signal.float_factorial(B is not present at runtime
-scipy.signal.hankel(B is not present at runtime
-scipy.signal.ifft(B is not present at runtime
-scipy.signal.lstsq(B is not present at runtime
-scipy.signal.maxflat(B is not present at runtime
-scipy.signal.morlet(B is not present in stub
-scipy.signal.morlet2(B is not present in stub
-scipy.signal.next_fast_len(B is not present at runtime
-scipy.signal.normalize_axis_index(B is not present at runtime
-scipy.signal.odd_ext(B is not present at runtime
-scipy.signal.qmf(B is not present in stub
-scipy.signal.ricker(B is not present in stub
-scipy.signal.sinc(B is not present at runtime
-scipy.signal.solve(B is not present at runtime
-scipy.signal.symiirorder1_ic(B is not present at runtime
-scipy.signal.symiirorder2_ic_bwd(B is not present at runtime
-scipy.signal.symiirorder2_ic_fwd(B is not present at runtime
-scipy.signal.symiirorder_nd(B is not present at runtime
-scipy.signal.toeplitz(B is not present at runtime
-scipy.signal.yulewalk(B is not present at runtime
-scipy.signal.zero_ext(B is not present at runtime
-scipy.signal._filter_design.abs(B variable differs from runtime type numpy.ufunc
-scipy.signal._filter_design.comb(B is inconsistent, stub argument "n" differs from runtime argument "N"
-scipy.signal._fir_filter_design.solve(B is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-scipy.signal._max_len_seq_inner.__pythran__(B is not present in stub
-scipy.signal._short_time_fft.FFT_MODE_TYPE(B is not a Union
-scipy.signal._short_time_fft.PAD_TYPE(B is not a Union
-scipy.signal._signaltools.cmplx_sort(B is not present in stub
-scipy.signal._spectral.__pythran__(B is not present in stub
-scipy.signal._spline.cspline2d(B is not present in stub
-scipy.signal._spline.qspline2d(B is not present in stub
-scipy.signal._spline.symiirorder1(B is not present in stub
-scipy.signal._spline.symiirorder1_ic(B is not present at runtime
-scipy.signal._spline.symiirorder2(B is not present in stub
-scipy.signal._spline.symiirorder2_ic_bwd(B is not present at runtime
-scipy.signal._spline.symiirorder2_ic_fwd(B is not present at runtime
-scipy.signal._spline_filters(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.signal._spline_filters'
-scipy.signal._wavelets.cascade(B is not present in stub
-scipy.signal._wavelets.cwt(B is not present in stub
-scipy.signal._wavelets.daub(B is not present in stub
-scipy.signal._wavelets.morlet(B is not present in stub
-scipy.signal._wavelets.morlet2(B is not present in stub
-scipy.signal._wavelets.qmf(B is not present in stub
-scipy.signal._wavelets.ricker(B is not present in stub
-scipy.signal.bsplines.cspline1d(B is not present in stub
-scipy.signal.bsplines.cspline1d_eval(B is not present in stub
-scipy.signal.bsplines.cspline2d(B is not present in stub
-scipy.signal.bsplines.gauss_spline(B is not present in stub
-scipy.signal.bsplines.qspline1d(B is not present in stub
-scipy.signal.bsplines.qspline1d_eval(B is not present in stub
-scipy.signal.bsplines.sepfir2d(B is not present in stub
-scipy.signal.bsplines.spline_filter(B is not present in stub
-scipy.signal.filter_design.BadCoefficients(B is not present in stub
-scipy.signal.filter_design.band_stop_obj(B is not present in stub
-scipy.signal.filter_design.bessel(B is not present in stub
-scipy.signal.filter_design.besselap(B is not present in stub
-scipy.signal.filter_design.bilinear(B is not present in stub
-scipy.signal.filter_design.bilinear_zpk(B is not present in stub
-scipy.signal.filter_design.buttap(B is not present in stub
-scipy.signal.filter_design.butter(B is not present in stub
-scipy.signal.filter_design.buttord(B is not present in stub
-scipy.signal.filter_design.cheb1ap(B is not present in stub
-scipy.signal.filter_design.cheb1ord(B is not present in stub
-scipy.signal.filter_design.cheb2ap(B is not present in stub
-scipy.signal.filter_design.cheb2ord(B is not present in stub
-scipy.signal.filter_design.cheby1(B is not present in stub
-scipy.signal.filter_design.cheby2(B is not present in stub
-scipy.signal.filter_design.ellip(B is not present in stub
-scipy.signal.filter_design.ellipap(B is not present in stub
-scipy.signal.filter_design.ellipord(B is not present in stub
-scipy.signal.filter_design.findfreqs(B is not present in stub
-scipy.signal.filter_design.freqs(B is not present in stub
-scipy.signal.filter_design.freqs_zpk(B is not present in stub
-scipy.signal.filter_design.freqz(B is not present in stub
-scipy.signal.filter_design.freqz_zpk(B is not present in stub
-scipy.signal.filter_design.gammatone(B is not present in stub
-scipy.signal.filter_design.group_delay(B is not present in stub
-scipy.signal.filter_design.iircomb(B is not present in stub
-scipy.signal.filter_design.iirdesign(B is not present in stub
-scipy.signal.filter_design.iirfilter(B is not present in stub
-scipy.signal.filter_design.iirnotch(B is not present in stub
-scipy.signal.filter_design.iirpeak(B is not present in stub
-scipy.signal.filter_design.lp2bp(B is not present in stub
-scipy.signal.filter_design.lp2bp_zpk(B is not present in stub
-scipy.signal.filter_design.lp2bs(B is not present in stub
-scipy.signal.filter_design.lp2bs_zpk(B is not present in stub
-scipy.signal.filter_design.lp2hp(B is not present in stub
-scipy.signal.filter_design.lp2hp_zpk(B is not present in stub
-scipy.signal.filter_design.lp2lp(B is not present in stub
-scipy.signal.filter_design.lp2lp_zpk(B is not present in stub
-scipy.signal.filter_design.normalize(B is not present in stub
-scipy.signal.filter_design.sos2tf(B is not present in stub
-scipy.signal.filter_design.sos2zpk(B is not present in stub
-scipy.signal.filter_design.sosfreqz(B is not present in stub
-scipy.signal.filter_design.tf2sos(B is not present in stub
-scipy.signal.filter_design.tf2zpk(B is not present in stub
-scipy.signal.filter_design.zpk2sos(B is not present in stub
-scipy.signal.filter_design.zpk2tf(B is not present in stub
-scipy.signal.fir_filter_design.firls(B is not present in stub
-scipy.signal.fir_filter_design.firwin(B is not present in stub
-scipy.signal.fir_filter_design.firwin2(B is not present in stub
-scipy.signal.fir_filter_design.kaiser_atten(B is not present in stub
-scipy.signal.fir_filter_design.kaiser_beta(B is not present in stub
-scipy.signal.fir_filter_design.kaiserord(B is not present in stub
-scipy.signal.fir_filter_design.minimum_phase(B is not present in stub
-scipy.signal.fir_filter_design.remez(B is not present in stub
-scipy.signal.lti_conversion.abcd_normalize(B is not present in stub
-scipy.signal.lti_conversion.cont2discrete(B is not present in stub
-scipy.signal.lti_conversion.normalize(B is not present in stub
-scipy.signal.lti_conversion.ss2tf(B is not present in stub
-scipy.signal.lti_conversion.ss2zpk(B is not present in stub
-scipy.signal.lti_conversion.tf2ss(B is not present in stub
-scipy.signal.lti_conversion.tf2zpk(B is not present in stub
-scipy.signal.lti_conversion.zpk2ss(B is not present in stub
-scipy.signal.lti_conversion.zpk2tf(B is not present in stub
-scipy.signal.ltisys.StateSpace(B is not present in stub
-scipy.signal.ltisys.TransferFunction(B is not present in stub
-scipy.signal.ltisys.ZerosPolesGain(B is not present in stub
-scipy.signal.ltisys.abcd_normalize(B is not present in stub
-scipy.signal.ltisys.bode(B is not present in stub
-scipy.signal.ltisys.cont2discrete(B is not present in stub
-scipy.signal.ltisys.dbode(B is not present in stub
-scipy.signal.ltisys.dfreqresp(B is not present in stub
-scipy.signal.ltisys.dimpulse(B is not present in stub
-scipy.signal.ltisys.dlsim(B is not present in stub
-scipy.signal.ltisys.dlti(B is not present in stub
-scipy.signal.ltisys.dstep(B is not present in stub
-scipy.signal.ltisys.freqresp(B is not present in stub
-scipy.signal.ltisys.freqs(B is not present in stub
-scipy.signal.ltisys.freqs_zpk(B is not present in stub
-scipy.signal.ltisys.freqz(B is not present in stub
-scipy.signal.ltisys.freqz_zpk(B is not present in stub
-scipy.signal.ltisys.impulse(B is not present in stub
-scipy.signal.ltisys.lsim(B is not present in stub
-scipy.signal.ltisys.lti(B is not present in stub
-scipy.signal.ltisys.normalize(B is not present in stub
-scipy.signal.ltisys.place_poles(B is not present in stub
-scipy.signal.ltisys.ss2tf(B is not present in stub
-scipy.signal.ltisys.ss2zpk(B is not present in stub
-scipy.signal.ltisys.step(B is not present in stub
-scipy.signal.ltisys.tf2ss(B is not present in stub
-scipy.signal.ltisys.tf2zpk(B is not present in stub
-scipy.signal.ltisys.zpk2ss(B is not present in stub
-scipy.signal.ltisys.zpk2tf(B is not present in stub
-scipy.signal.signaltools.cheby1(B is not present in stub
-scipy.signal.signaltools.choose_conv_method(B is not present in stub
-scipy.signal.signaltools.cmplx_sort(B is not present in stub
-scipy.signal.signaltools.convolve(B is not present in stub
-scipy.signal.signaltools.convolve2d(B is not present in stub
-scipy.signal.signaltools.correlate(B is not present in stub
-scipy.signal.signaltools.correlate2d(B is not present in stub
-scipy.signal.signaltools.correlation_lags(B is not present in stub
-scipy.signal.signaltools.decimate(B is not present in stub
-scipy.signal.signaltools.deconvolve(B is not present in stub
-scipy.signal.signaltools.detrend(B is not present in stub
-scipy.signal.signaltools.dlti(B is not present in stub
-scipy.signal.signaltools.fftconvolve(B is not present in stub
-scipy.signal.signaltools.filtfilt(B is not present in stub
-scipy.signal.signaltools.firwin(B is not present in stub
-scipy.signal.signaltools.get_window(B is not present in stub
-scipy.signal.signaltools.hilbert(B is not present in stub
-scipy.signal.signaltools.hilbert2(B is not present in stub
-scipy.signal.signaltools.invres(B is not present in stub
-scipy.signal.signaltools.invresz(B is not present in stub
-scipy.signal.signaltools.lfilter(B is not present in stub
-scipy.signal.signaltools.lfilter_zi(B is not present in stub
-scipy.signal.signaltools.lfiltic(B is not present in stub
-scipy.signal.signaltools.medfilt(B is not present in stub
-scipy.signal.signaltools.medfilt2d(B is not present in stub
-scipy.signal.signaltools.oaconvolve(B is not present in stub
-scipy.signal.signaltools.order_filter(B is not present in stub
-scipy.signal.signaltools.resample(B is not present in stub
-scipy.signal.signaltools.resample_poly(B is not present in stub
-scipy.signal.signaltools.residue(B is not present in stub
-scipy.signal.signaltools.residuez(B is not present in stub
-scipy.signal.signaltools.sosfilt(B is not present in stub
-scipy.signal.signaltools.sosfilt_zi(B is not present in stub
-scipy.signal.signaltools.sosfiltfilt(B is not present in stub
-scipy.signal.signaltools.unique_roots(B is not present in stub
-scipy.signal.signaltools.upfirdn(B is not present in stub
-scipy.signal.signaltools.vectorstrength(B is not present in stub
-scipy.signal.signaltools.wiener(B is not present in stub
-scipy.signal.spectral.check_COLA(B is not present in stub
-scipy.signal.spectral.check_NOLA(B is not present in stub
-scipy.signal.spectral.coherence(B is not present in stub
-scipy.signal.spectral.csd(B is not present in stub
-scipy.signal.spectral.get_window(B is not present in stub
-scipy.signal.spectral.istft(B is not present in stub
-scipy.signal.spectral.lombscargle(B is not present in stub
-scipy.signal.spectral.periodogram(B is not present in stub
-scipy.signal.spectral.spectrogram(B is not present in stub
-scipy.signal.spectral.stft(B is not present in stub
-scipy.signal.spectral.welch(B is not present in stub
-scipy.signal.spline.cspline2d(B is not present in stub
-scipy.signal.spline.qspline2d(B is not present in stub
-scipy.signal.spline.sepfir2d(B is not present in stub
-scipy.signal.spline.symiirorder1(B is not present in stub
-scipy.signal.spline.symiirorder2(B is not present in stub
-scipy.signal.waveforms.chirp(B is not present in stub
-scipy.signal.waveforms.gausspulse(B is not present in stub
-scipy.signal.waveforms.sawtooth(B is not present in stub
-scipy.signal.waveforms.square(B is not present in stub
-scipy.signal.waveforms.sweep_poly(B is not present in stub
-scipy.signal.waveforms.unit_impulse(B is not present in stub
-scipy.signal.wavelets.cascade(B is not present in stub
-scipy.signal.wavelets.convolve(B is not present in stub
-scipy.signal.wavelets.cwt(B is not present in stub
-scipy.signal.wavelets.daub(B is not present in stub
-scipy.signal.wavelets.morlet(B is not present in stub
-scipy.signal.wavelets.morlet2(B is not present in stub
-scipy.signal.wavelets.qmf(B is not present in stub
-scipy.signal.wavelets.ricker(B is not present in stub
-scipy.signal.windows.windows.barthann(B is not present in stub
-scipy.signal.windows.windows.bartlett(B is not present in stub
-scipy.signal.windows.windows.blackman(B is not present in stub
-scipy.signal.windows.windows.blackmanharris(B is not present in stub
-scipy.signal.windows.windows.bohman(B is not present in stub
-scipy.signal.windows.windows.boxcar(B is not present in stub
-scipy.signal.windows.windows.chebwin(B is not present in stub
-scipy.signal.windows.windows.cosine(B is not present in stub
-scipy.signal.windows.windows.dpss(B is not present in stub
-scipy.signal.windows.windows.exponential(B is not present in stub
-scipy.signal.windows.windows.flattop(B is not present in stub
-scipy.signal.windows.windows.gaussian(B is not present in stub
-scipy.signal.windows.windows.general_cosine(B is not present in stub
-scipy.signal.windows.windows.general_gaussian(B is not present in stub
-scipy.signal.windows.windows.general_hamming(B is not present in stub
-scipy.signal.windows.windows.get_window(B is not present in stub
-scipy.signal.windows.windows.hamming(B is not present in stub
-scipy.signal.windows.windows.hann(B is not present in stub
-scipy.signal.windows.windows.kaiser(B is not present in stub
-scipy.signal.windows.windows.nuttall(B is not present in stub
-scipy.signal.windows.windows.parzen(B is not present in stub
-scipy.signal.windows.windows.taylor(B is not present in stub
-scipy.signal.windows.windows.triang(B is not present in stub
-scipy.signal.windows.windows.tukey(B is not present in stub
-scipy.sparse.PICKLE_KWARGS(B is not present at runtime
-scipy.sparse.linalg(B is not present in stub
-scipy.sparse.spmatrix.todense(B is not present at runtime
-scipy.sparse._base._spbase.__init__(B is inconsistent, runtime argument "maxprint" is not keyword-only
-scipy.sparse._base._spbase.count_nonzero(B is inconsistent, runtime does not have argument "axis"
-scipy.sparse._base.spmatrix.todense(B is not present at runtime
-scipy.sparse._bsr._bsr_base.__init__(B is inconsistent, runtime does not have argument "maxprint"
-scipy.sparse._bsr._bsr_base.tocoo(B is inconsistent, runtime argument "copy" has a default value of True, which is different from stub argument default False
-scipy.sparse._compressed._cs_matrix.__init__(B is inconsistent, runtime does not have argument "maxprint"
-scipy.sparse._compressed._cs_matrix.tocoo(B is inconsistent, runtime argument "copy" has a default value of True, which is different from stub argument default False
-scipy.sparse._coo._coo_base.__init__(B is inconsistent, runtime does not have argument "maxprint"
-scipy.sparse._csr._csr_base.tobsr(B is inconsistent, runtime argument "copy" has a default value of True, which is different from stub argument default False
-scipy.sparse._data._data_matrix.__init__(B is inconsistent, runtime does not have argument "maxprint"
-scipy.sparse._data._data_matrix.arcsin(B is not present in stub
-scipy.sparse._data._data_matrix.arcsinh(B is not present in stub
-scipy.sparse._data._data_matrix.arctan(B is not present in stub
-scipy.sparse._data._data_matrix.arctanh(B is not present in stub
-scipy.sparse._data._data_matrix.ceil(B is not present in stub
-scipy.sparse._data._data_matrix.count_nonzero(B is inconsistent, runtime does not have argument "axis"
-scipy.sparse._data._data_matrix.deg2rad(B is not present in stub
-scipy.sparse._data._data_matrix.expm1(B is not present in stub
-scipy.sparse._data._data_matrix.floor(B is not present in stub
-scipy.sparse._data._data_matrix.log1p(B is not present in stub
-scipy.sparse._data._data_matrix.rad2deg(B is not present in stub
-scipy.sparse._data._data_matrix.rint(B is not present in stub
-scipy.sparse._data._data_matrix.sign(B is not present in stub
-scipy.sparse._data._data_matrix.sin(B is not present in stub
-scipy.sparse._data._data_matrix.sinh(B is not present in stub
-scipy.sparse._data._data_matrix.sqrt(B is not present in stub
-scipy.sparse._data._data_matrix.tan(B is not present in stub
-scipy.sparse._data._data_matrix.tanh(B is not present in stub
-scipy.sparse._data._data_matrix.trunc(B is not present in stub
-scipy.sparse._data._minmax_mixin.argmax(B is inconsistent, runtime does not have argument "explicit"
-scipy.sparse._data._minmax_mixin.argmin(B is inconsistent, runtime does not have argument "explicit"
-scipy.sparse._data._minmax_mixin.max(B is inconsistent, runtime does not have argument "explicit"
-scipy.sparse._data._minmax_mixin.min(B is inconsistent, runtime does not have argument "explicit"
-scipy.sparse._data._minmax_mixin.nanmax(B is inconsistent, runtime does not have argument "explicit"
-scipy.sparse._data._minmax_mixin.nanmin(B is inconsistent, runtime does not have argument "explicit"
-scipy.sparse._dia._dia_base.__init__(B is inconsistent, runtime does not have argument "maxprint"
-scipy.sparse._dia._dia_base.count_nonzero(B is inconsistent, runtime does not have argument "axis"
-scipy.sparse._dok._dok_base.__init__(B is inconsistent, runtime does not have argument "maxprint"
-scipy.sparse._dok._dok_base.count_nonzero(B is inconsistent, runtime does not have argument "axis"
-scipy.sparse._generate_sparsetools(B failed to import. Maybe install the runtime package or alter PYTHONPATH? ModuleNotFoundError: No module named 'scipy.sparse._generate_sparsetools'
-scipy.sparse._index.INT_TYPES(B is not present in stub
-scipy.sparse._lil._lil_base.__init__(B is inconsistent, runtime does not have argument "maxprint"
-scipy.sparse._lil._lil_base.count_nonzero(B is inconsistent, runtime does not have argument "axis"
-scipy.sparse._matrix.spmatrix.todense(B is not present at runtime
-scipy.sparse._sputils.convert_pydata_sparse_to_scipy(B is inconsistent, runtime does not have argument "accept_fv"
-scipy.sparse.base.MAXPRINT(B is not present in stub
-scipy.sparse.base.SparseEfficiencyWarning(B is not present in stub
-scipy.sparse.base.SparseFormatWarning(B is not present in stub
-scipy.sparse.base.SparseWarning(B is not present in stub
-scipy.sparse.base.asmatrix(B is not present in stub
-scipy.sparse.base.check_reshape_kwargs(B is not present in stub
-scipy.sparse.base.check_shape(B is not present in stub
-scipy.sparse.base.get_sum_dtype(B is not present in stub
-scipy.sparse.base.isdense(B is not present in stub
-scipy.sparse.base.isscalarlike(B is not present in stub
-scipy.sparse.base.issparse(B is not present in stub
-scipy.sparse.base.isspmatrix(B is not present in stub
-scipy.sparse.base.spmatrix(B is not present in stub
-scipy.sparse.base.validateaxis(B is not present in stub
-scipy.sparse.bsr.bsr_matmat(B is not present in stub
-scipy.sparse.bsr.bsr_matrix(B is not present in stub
-scipy.sparse.bsr.bsr_matvec(B is not present in stub
-scipy.sparse.bsr.bsr_matvecs(B is not present in stub
-scipy.sparse.bsr.bsr_sort_indices(B is not present in stub
-scipy.sparse.bsr.bsr_tocsr(B is not present in stub
-scipy.sparse.bsr.bsr_transpose(B is not present in stub
-scipy.sparse.bsr.check_shape(B is not present in stub
-scipy.sparse.bsr.csr_matmat_maxnnz(B is not present in stub
-scipy.sparse.bsr.getdata(B is not present in stub
-scipy.sparse.bsr.getdtype(B is not present in stub
-scipy.sparse.bsr.isshape(B is not present in stub
-scipy.sparse.bsr.isspmatrix_bsr(B is not present in stub
-scipy.sparse.bsr.spmatrix(B is not present in stub
-scipy.sparse.bsr.to_native(B is not present in stub
-scipy.sparse.bsr.upcast(B is not present in stub
-scipy.sparse.bsr.warn(B is not present in stub
-scipy.sparse.compressed.IndexMixin(B is not present in stub
-scipy.sparse.compressed.SparseEfficiencyWarning(B is not present in stub
-scipy.sparse.compressed.check_shape(B is not present in stub
-scipy.sparse.compressed.csr_column_index1(B is not present in stub
-scipy.sparse.compressed.csr_column_index2(B is not present in stub
-scipy.sparse.compressed.csr_row_index(B is not present in stub
-scipy.sparse.compressed.csr_row_slice(B is not present in stub
-scipy.sparse.compressed.csr_sample_offsets(B is not present in stub
-scipy.sparse.compressed.csr_sample_values(B is not present in stub
-scipy.sparse.compressed.csr_todense(B is not present in stub
-scipy.sparse.compressed.downcast_intp_index(B is not present in stub
-scipy.sparse.compressed.get_csr_submatrix(B is not present in stub
-scipy.sparse.compressed.get_sum_dtype(B is not present in stub
-scipy.sparse.compressed.getdtype(B is not present in stub
-scipy.sparse.compressed.is_pydata_spmatrix(B is not present in stub
-scipy.sparse.compressed.isdense(B is not present in stub
-scipy.sparse.compressed.isintlike(B is not present in stub
-scipy.sparse.compressed.isscalarlike(B is not present in stub
-scipy.sparse.compressed.isshape(B is not present in stub
-scipy.sparse.compressed.operator(B is not present in stub
-scipy.sparse.compressed.to_native(B is not present in stub
-scipy.sparse.compressed.upcast(B is not present in stub
-scipy.sparse.compressed.upcast_char(B is not present in stub
-scipy.sparse.compressed.warn(B is not present in stub
-scipy.sparse.construct.block_diag(B is not present in stub
-scipy.sparse.construct.bmat(B is not present in stub
-scipy.sparse.construct.bsr_matrix(B is not present in stub
-scipy.sparse.construct.check_random_state(B is not present in stub
-scipy.sparse.construct.coo_matrix(B is not present in stub
-scipy.sparse.construct.csc_matrix(B is not present in stub
-scipy.sparse.construct.csr_hstack(B is not present in stub
-scipy.sparse.construct.csr_matrix(B is not present in stub
-scipy.sparse.construct.dia_matrix(B is not present in stub
-scipy.sparse.construct.diags(B is not present in stub
-scipy.sparse.construct.eye(B is not present in stub
-scipy.sparse.construct.get_index_dtype(B is not present in stub
-scipy.sparse.construct.hstack(B is not present in stub
-scipy.sparse.construct.identity(B is not present in stub
-scipy.sparse.construct.isscalarlike(B is not present in stub
-scipy.sparse.construct.issparse(B is not present in stub
-scipy.sparse.construct.kron(B is not present in stub
-scipy.sparse.construct.kronsum(B is not present in stub
-scipy.sparse.construct.numbers(B is not present in stub
-scipy.sparse.construct.rand(B is not present in stub
-scipy.sparse.construct.random(B is not present in stub
-scipy.sparse.construct.rng_integers(B is not present in stub
-scipy.sparse.construct.spdiags(B is not present in stub
-scipy.sparse.construct.upcast(B is not present in stub
-scipy.sparse.construct.vstack(B is not present in stub
-scipy.sparse.coo.SparseEfficiencyWarning(B is not present in stub
-scipy.sparse.coo.check_reshape_kwargs(B is not present in stub
-scipy.sparse.coo.check_shape(B is not present in stub
-scipy.sparse.coo.coo_matrix(B is not present in stub
-scipy.sparse.coo.coo_matvec(B is not present in stub
-scipy.sparse.coo.coo_tocsr(B is not present in stub
-scipy.sparse.coo.coo_todense(B is not present in stub
-scipy.sparse.coo.downcast_intp_index(B is not present in stub
-scipy.sparse.coo.getdata(B is not present in stub
-scipy.sparse.coo.getdtype(B is not present in stub
-scipy.sparse.coo.isshape(B is not present in stub
-scipy.sparse.coo.isspmatrix_coo(B is not present in stub
-scipy.sparse.coo.operator(B is not present in stub
-scipy.sparse.coo.spmatrix(B is not present in stub
-scipy.sparse.coo.to_native(B is not present in stub
-scipy.sparse.coo.upcast(B is not present in stub
-scipy.sparse.coo.upcast_char(B is not present in stub
-scipy.sparse.coo.warn(B is not present in stub
-scipy.sparse.csc.csc_matrix(B is not present in stub
-scipy.sparse.csc.csc_tocsr(B is not present in stub
-scipy.sparse.csc.expandptr(B is not present in stub
-scipy.sparse.csc.isspmatrix_csc(B is not present in stub
-scipy.sparse.csc.spmatrix(B is not present in stub
-scipy.sparse.csc.upcast(B is not present in stub
-scipy.sparse.csgraph.NegativeCycleError.__init__(B is inconsistent, stub does not have argument "message"
-scipy.sparse.csgraph.NegativeCycleError.__init__(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.bellman_ford(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.bellman_ford(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.bellman_ford(B is inconsistent, stub does not have argument "indices"
-scipy.sparse.csgraph.bellman_ford(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph.bellman_ford(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph.bellman_ford(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.bellman_ford(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.breadth_first_order(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.breadth_first_order(B is inconsistent, stub does not have argument "i_start"
-scipy.sparse.csgraph.breadth_first_order(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.breadth_first_order(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph.breadth_first_order(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.breadth_first_order(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.breadth_first_tree(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.breadth_first_tree(B is inconsistent, stub does not have argument "i_start"
-scipy.sparse.csgraph.breadth_first_tree(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.breadth_first_tree(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.breadth_first_tree(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.connected_components(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.connected_components(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.connected_components(B is inconsistent, stub does not have argument "connection"
-scipy.sparse.csgraph.connected_components(B is inconsistent, stub does not have argument "return_labels"
-scipy.sparse.csgraph.connected_components(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.connected_components(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.construct_dist_matrix(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph.construct_dist_matrix(B is inconsistent, stub does not have argument "predecessors"
-scipy.sparse.csgraph.construct_dist_matrix(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.construct_dist_matrix(B is inconsistent, stub does not have argument "null_value"
-scipy.sparse.csgraph.construct_dist_matrix(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.construct_dist_matrix(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.csgraph_from_dense(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph.csgraph_from_dense(B is inconsistent, stub does not have argument "null_value"
-scipy.sparse.csgraph.csgraph_from_dense(B is inconsistent, stub does not have argument "nan_null"
-scipy.sparse.csgraph.csgraph_from_dense(B is inconsistent, stub does not have argument "infinity_null"
-scipy.sparse.csgraph.csgraph_from_dense(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.csgraph_from_dense(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.csgraph_from_masked(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph.csgraph_from_masked(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.csgraph_from_masked(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "null_value"
-scipy.sparse.csgraph.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "nan_null"
-scipy.sparse.csgraph.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "infinity_null"
-scipy.sparse.csgraph.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "copy"
-scipy.sparse.csgraph.csgraph_masked_from_dense(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.csgraph_masked_from_dense(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.csgraph_to_dense(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.csgraph_to_dense(B is inconsistent, stub does not have argument "null_value"
-scipy.sparse.csgraph.csgraph_to_dense(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.csgraph_to_dense(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.csgraph_to_masked(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.csgraph_to_masked(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.csgraph_to_masked(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.depth_first_order(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.depth_first_order(B is inconsistent, stub does not have argument "i_start"
-scipy.sparse.csgraph.depth_first_order(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.depth_first_order(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph.depth_first_order(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.depth_first_order(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.depth_first_tree(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.depth_first_tree(B is inconsistent, stub does not have argument "i_start"
-scipy.sparse.csgraph.depth_first_tree(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.depth_first_tree(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.depth_first_tree(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, stub does not have argument "indices"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, stub does not have argument "limit"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, stub does not have argument "min_only"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.dijkstra(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.floyd_warshall(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.floyd_warshall(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.floyd_warshall(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph.floyd_warshall(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph.floyd_warshall(B is inconsistent, stub does not have argument "overwrite"
-scipy.sparse.csgraph.floyd_warshall(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.floyd_warshall(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.johnson(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.johnson(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.johnson(B is inconsistent, stub does not have argument "indices"
-scipy.sparse.csgraph.johnson(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph.johnson(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph.johnson(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.johnson(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.maximum_bipartite_matching(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph.maximum_bipartite_matching(B is inconsistent, stub does not have argument "perm_type"
-scipy.sparse.csgraph.maximum_bipartite_matching(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.maximum_bipartite_matching(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.maximum_flow(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.maximum_flow(B is inconsistent, stub does not have argument "source"
-scipy.sparse.csgraph.maximum_flow(B is inconsistent, stub does not have argument "sink"
-scipy.sparse.csgraph.maximum_flow(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.maximum_flow(B is inconsistent, stub does not have argument "method"
-scipy.sparse.csgraph.maximum_flow(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.min_weight_full_bipartite_matching(B is inconsistent, stub does not have argument "biadjacency_matrix"
-scipy.sparse.csgraph.min_weight_full_bipartite_matching(B is inconsistent, stub does not have argument "maximize"
-scipy.sparse.csgraph.min_weight_full_bipartite_matching(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.min_weight_full_bipartite_matching(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.minimum_spanning_tree(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.minimum_spanning_tree(B is inconsistent, stub does not have argument "overwrite"
-scipy.sparse.csgraph.minimum_spanning_tree(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.minimum_spanning_tree(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.reconstruct_path(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.reconstruct_path(B is inconsistent, stub does not have argument "predecessors"
-scipy.sparse.csgraph.reconstruct_path(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.reconstruct_path(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.reconstruct_path(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.reverse_cuthill_mckee(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph.reverse_cuthill_mckee(B is inconsistent, stub does not have argument "symmetric_mode"
-scipy.sparse.csgraph.reverse_cuthill_mckee(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.reverse_cuthill_mckee(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, stub does not have argument "method"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, stub does not have argument "overwrite"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, stub does not have argument "indices"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.shortest_path(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.structural_rank(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph.structural_rank(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.structural_rank(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph.yen(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph.yen(B is inconsistent, stub does not have argument "source"
-scipy.sparse.csgraph.yen(B is inconsistent, stub does not have argument "sink"
-scipy.sparse.csgraph.yen(B is inconsistent, stub does not have argument "K"
-scipy.sparse.csgraph.yen(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph.yen(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph.yen(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph.yen(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph.yen(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._flow.MaximumFlowResult(B is not present in stub
-scipy.sparse.csgraph._flow.maximum_flow(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._flow.maximum_flow(B is inconsistent, stub does not have argument "source"
-scipy.sparse.csgraph._flow.maximum_flow(B is inconsistent, stub does not have argument "sink"
-scipy.sparse.csgraph._flow.maximum_flow(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._flow.maximum_flow(B is inconsistent, stub does not have argument "method"
-scipy.sparse.csgraph._flow.maximum_flow(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._matching.maximum_bipartite_matching(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph._matching.maximum_bipartite_matching(B is inconsistent, stub does not have argument "perm_type"
-scipy.sparse.csgraph._matching.maximum_bipartite_matching(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._matching.maximum_bipartite_matching(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._matching.min_weight_full_bipartite_matching(B is inconsistent, stub does not have argument "biadjacency_matrix"
-scipy.sparse.csgraph._matching.min_weight_full_bipartite_matching(B is inconsistent, stub does not have argument "maximize"
-scipy.sparse.csgraph._matching.min_weight_full_bipartite_matching(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._matching.min_weight_full_bipartite_matching(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._min_spanning_tree.minimum_spanning_tree(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._min_spanning_tree.minimum_spanning_tree(B is inconsistent, stub does not have argument "overwrite"
-scipy.sparse.csgraph._min_spanning_tree.minimum_spanning_tree(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._min_spanning_tree.minimum_spanning_tree(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._reordering.reverse_cuthill_mckee(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph._reordering.reverse_cuthill_mckee(B is inconsistent, stub does not have argument "symmetric_mode"
-scipy.sparse.csgraph._reordering.reverse_cuthill_mckee(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._reordering.reverse_cuthill_mckee(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._reordering.structural_rank(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph._reordering.structural_rank(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._reordering.structural_rank(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._shortest_path.NegativeCycleError.__init__(B is inconsistent, stub does not have argument "message"
-scipy.sparse.csgraph._shortest_path.NegativeCycleError.__init__(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._shortest_path.bellman_ford(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._shortest_path.bellman_ford(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._shortest_path.bellman_ford(B is inconsistent, stub does not have argument "indices"
-scipy.sparse.csgraph._shortest_path.bellman_ford(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph._shortest_path.bellman_ford(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph._shortest_path.bellman_ford(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._shortest_path.bellman_ford(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, stub does not have argument "indices"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, stub does not have argument "limit"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, stub does not have argument "min_only"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._shortest_path.dijkstra(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._shortest_path.floyd_warshall(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._shortest_path.floyd_warshall(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._shortest_path.floyd_warshall(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph._shortest_path.floyd_warshall(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph._shortest_path.floyd_warshall(B is inconsistent, stub does not have argument "overwrite"
-scipy.sparse.csgraph._shortest_path.floyd_warshall(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._shortest_path.floyd_warshall(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._shortest_path.johnson(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._shortest_path.johnson(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._shortest_path.johnson(B is inconsistent, stub does not have argument "indices"
-scipy.sparse.csgraph._shortest_path.johnson(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph._shortest_path.johnson(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph._shortest_path.johnson(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._shortest_path.johnson(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, stub does not have argument "method"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, stub does not have argument "overwrite"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, stub does not have argument "indices"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._shortest_path.shortest_path(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, stub does not have argument "source"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, stub does not have argument "sink"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, stub does not have argument "K"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, stub does not have argument "unweighted"
-scipy.sparse.csgraph._shortest_path.yen(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._tools.construct_dist_matrix(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph._tools.construct_dist_matrix(B is inconsistent, stub does not have argument "predecessors"
-scipy.sparse.csgraph._tools.construct_dist_matrix(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._tools.construct_dist_matrix(B is inconsistent, stub does not have argument "null_value"
-scipy.sparse.csgraph._tools.construct_dist_matrix(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._tools.construct_dist_matrix(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._tools.csgraph_from_dense(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph._tools.csgraph_from_dense(B is inconsistent, stub does not have argument "null_value"
-scipy.sparse.csgraph._tools.csgraph_from_dense(B is inconsistent, stub does not have argument "nan_null"
-scipy.sparse.csgraph._tools.csgraph_from_dense(B is inconsistent, stub does not have argument "infinity_null"
-scipy.sparse.csgraph._tools.csgraph_from_dense(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._tools.csgraph_from_dense(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._tools.csgraph_from_masked(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph._tools.csgraph_from_masked(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._tools.csgraph_from_masked(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._tools.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "graph"
-scipy.sparse.csgraph._tools.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "null_value"
-scipy.sparse.csgraph._tools.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "nan_null"
-scipy.sparse.csgraph._tools.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "infinity_null"
-scipy.sparse.csgraph._tools.csgraph_masked_from_dense(B is inconsistent, stub does not have argument "copy"
-scipy.sparse.csgraph._tools.csgraph_masked_from_dense(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._tools.csgraph_masked_from_dense(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._tools.csgraph_to_dense(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._tools.csgraph_to_dense(B is inconsistent, stub does not have argument "null_value"
-scipy.sparse.csgraph._tools.csgraph_to_dense(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._tools.csgraph_to_dense(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._tools.csgraph_to_masked(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._tools.csgraph_to_masked(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._tools.csgraph_to_masked(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._tools.reconstruct_path(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._tools.reconstruct_path(B is inconsistent, stub does not have argument "predecessors"
-scipy.sparse.csgraph._tools.reconstruct_path(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._tools.reconstruct_path(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._tools.reconstruct_path(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._traversal.breadth_first_order(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._traversal.breadth_first_order(B is inconsistent, stub does not have argument "i_start"
-scipy.sparse.csgraph._traversal.breadth_first_order(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._traversal.breadth_first_order(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph._traversal.breadth_first_order(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._traversal.breadth_first_order(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._traversal.breadth_first_tree(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._traversal.breadth_first_tree(B is inconsistent, stub does not have argument "i_start"
-scipy.sparse.csgraph._traversal.breadth_first_tree(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._traversal.breadth_first_tree(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._traversal.breadth_first_tree(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._traversal.connected_components(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._traversal.connected_components(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._traversal.connected_components(B is inconsistent, stub does not have argument "connection"
-scipy.sparse.csgraph._traversal.connected_components(B is inconsistent, stub does not have argument "return_labels"
-scipy.sparse.csgraph._traversal.connected_components(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._traversal.connected_components(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._traversal.depth_first_order(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._traversal.depth_first_order(B is inconsistent, stub does not have argument "i_start"
-scipy.sparse.csgraph._traversal.depth_first_order(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._traversal.depth_first_order(B is inconsistent, stub does not have argument "return_predecessors"
-scipy.sparse.csgraph._traversal.depth_first_order(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._traversal.depth_first_order(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._traversal.depth_first_tree(B is inconsistent, stub does not have argument "csgraph"
-scipy.sparse.csgraph._traversal.depth_first_tree(B is inconsistent, stub does not have argument "i_start"
-scipy.sparse.csgraph._traversal.depth_first_tree(B is inconsistent, stub does not have argument "directed"
-scipy.sparse.csgraph._traversal.depth_first_tree(B is inconsistent, runtime does not have *args argument "args"
-scipy.sparse.csgraph._traversal.depth_first_tree(B is inconsistent, runtime does not have **kwargs argument "kwargs"
-scipy.sparse.csgraph._validation.DTYPE(B variable differs from runtime type type
-scipy.sparse.csr.csr_count_blocks(B is not present in stub
-scipy.sparse.csr.csr_matrix(B is not present in stub
-scipy.sparse.csr.csr_tobsr(B is not present in stub
-scipy.sparse.csr.csr_tocsc(B is not present in stub
-scipy.sparse.csr.get_csr_submatrix(B is not present in stub
-scipy.sparse.csr.isspmatrix_csr(B is not present in stub
-scipy.sparse.csr.spmatrix(B is not present in stub
-scipy.sparse.csr.upcast(B is not present in stub
-scipy.sparse.data.isscalarlike(B is not present in stub
-scipy.sparse.data.name(B is not present in stub
-scipy.sparse.data.npfunc(B is not present in stub
-scipy.sparse.data.validateaxis(B is not present in stub
-scipy.sparse.dia.check_shape(B is not present in stub
-scipy.sparse.dia.dia_matrix(B is not present in stub
-scipy.sparse.dia.dia_matvec(B is not present in stub
-scipy.sparse.dia.get_sum_dtype(B is not present in stub
-scipy.sparse.dia.getdtype(B is not present in stub
-scipy.sparse.dia.isshape(B is not present in stub
-scipy.sparse.dia.isspmatrix_dia(B is not present in stub
-scipy.sparse.dia.spmatrix(B is not present in stub
-scipy.sparse.dia.upcast_char(B is not present in stub
-scipy.sparse.dia.validateaxis(B is not present in stub
-scipy.sparse.dok.IndexMixin(B is not present in stub
-scipy.sparse.dok.check_shape(B is not present in stub
-scipy.sparse.dok.dok_matrix(B is not present in stub
-scipy.sparse.dok.getdtype(B is not present in stub
-scipy.sparse.dok.isdense(B is not present in stub
-scipy.sparse.dok.isintlike(B is not present in stub
-scipy.sparse.dok.isscalarlike(B is not present in stub
-scipy.sparse.dok.isshape(B is not present in stub
-scipy.sparse.dok.isspmatrix_dok(B is not present in stub
-scipy.sparse.dok.itertools(B is not present in stub
-scipy.sparse.dok.spmatrix(B is not present in stub
-scipy.sparse.dok.upcast(B is not present in stub
-scipy.sparse.dok.upcast_scalar(B is not present in stub
-scipy.sparse.extract.coo_matrix(B is not present in stub
-scipy.sparse.extract.find(B is not present in stub
-scipy.sparse.extract.tril(B is not present in stub
-scipy.sparse.extract.triu(B is not present in stub
-scipy.sparse.lil.isspmatrix_lil(B is not present in stub
-scipy.sparse.lil.lil_array(B is not present in stub
-scipy.sparse.lil.lil_matrix(B is not present in stub
-scipy.sparse.linalg.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.sparse.linalg.LinearOperator.dtype(B is not present at runtime
-scipy.sparse.linalg.MatrixRankWarning(B is not present in stub
-scipy.sparse.linalg.SuperLU(B cannot be subclassed at runtime, but isn't marked with @final in the stub
-scipy.sparse.linalg.factorized(B is not present in stub
-scipy.sparse.linalg.get_lapack_funcs(B is not present at runtime
-scipy.sparse.linalg.make_system(B is not present at runtime
-scipy.sparse.linalg.spilu(B is not present in stub
-scipy.sparse.linalg.splu(B is not present in stub
-scipy.sparse.linalg.spsolve(B is not present in stub
-scipy.sparse.linalg.spsolve_triangular(B is not present in stub
-scipy.sparse.linalg.use_solver(B is not present in stub
-scipy.sparse.linalg._dsolve.SuperLU(B cannot be subclassed at runtime, but isn't marked with @final in the stub
-scipy.sparse.linalg._dsolve.splu(B is inconsistent, runtime argument "options" has a default value of {}, which is different from stub argument default None
-scipy.sparse.linalg._dsolve._superlu.SuperLU(B cannot be subclassed at runtime, but isn't marked with @final in the stub
-scipy.sparse.linalg._dsolve._superlu.gssv(B is not present in stub
-scipy.sparse.linalg._dsolve._superlu.gstrf(B is not present in stub
-scipy.sparse.linalg._dsolve._superlu.gstrs(B is not present in stub
-scipy.sparse.linalg._dsolve.linsolve.splu(B is inconsistent, runtime argument "options" has a default value of {}, which is different from stub argument default None
-scipy.sparse.linalg._eigen.LinAlgError(B is not present at runtime
-scipy.sparse.linalg._eigen.LinearOperator(B is not present at runtime
-scipy.sparse.linalg._eigen.cho_factor(B is not present at runtime
-scipy.sparse.linalg._eigen.cho_solve(B is not present at runtime
-scipy.sparse.linalg._eigen.cholesky(B is not present at runtime
-scipy.sparse.linalg._eigen.eigh(B is not present at runtime
-scipy.sparse.linalg._eigen.inv(B is not present at runtime
-scipy.sparse.linalg._eigen.issparse(B is not present at runtime
-scipy.sparse.linalg._eigen._svds.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.sparse.linalg._eigen._svds.LinearOperator.dtype(B is not present at runtime
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, stub argument "a" differs from runtime argument "full_matrices"
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "compute_uv"
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "overwrite_a"
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "check_finite"
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "lapack_driver"
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, runtime argument "lapack_driver" has a default value of type 'gesdd', which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, runtime does not have argument "check_finite"
-scipy.sparse.linalg._eigen._svds.svd(B is inconsistent, runtime does not have argument "lapack_driver"
-scipy.sparse.linalg._eigen.arpack.arpack._SymmetricArpackParams.iterate(B is not present in stub
-scipy.sparse.linalg._eigen.arpack.arpack._UnsymmetricArpackParams.iterate(B is not present in stub
-scipy.sparse.linalg._eigen.lobpcg.LinAlgError(B is not present at runtime
-scipy.sparse.linalg._eigen.lobpcg.LinearOperator(B is not present at runtime
-scipy.sparse.linalg._eigen.lobpcg.cho_factor(B is not present at runtime
-scipy.sparse.linalg._eigen.lobpcg.cho_solve(B is not present at runtime
-scipy.sparse.linalg._eigen.lobpcg.cholesky(B is not present at runtime
-scipy.sparse.linalg._eigen.lobpcg.eigh(B is not present at runtime
-scipy.sparse.linalg._eigen.lobpcg.inv(B is not present at runtime
-scipy.sparse.linalg._eigen.lobpcg.issparse(B is not present at runtime
-scipy.sparse.linalg._eigen.lobpcg.lobpcg.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.sparse.linalg._eigen.lobpcg.lobpcg.LinearOperator.dtype(B is not present at runtime
-scipy.sparse.linalg._interface.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.sparse.linalg._interface.LinearOperator.dtype(B is not present at runtime
-scipy.sparse.linalg._isolve.LinearOperator(B is not present at runtime
-scipy.sparse.linalg._isolve.get_lapack_funcs(B is not present at runtime
-scipy.sparse.linalg._isolve.make_system(B is not present at runtime
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, stub argument "a" differs from runtime argument "overwrite_a"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "lwork"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, runtime argument "lwork" has a default value of type None, which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, stub argument "overwrite_a" differs from runtime argument "mode"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, runtime argument "mode" has a default value of type 'full', which is incompatible with stub argument type bool. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, stub argument "lwork" differs from runtime argument "pivoting"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, stub argument "lwork" differs from runtime argument "check_finite"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, runtime does not have argument "mode"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, runtime does not have argument "mode"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, runtime does not have argument "pivoting"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, runtime does not have argument "pivoting"
-scipy.sparse.linalg._isolve._gcrotmk.qr(B is inconsistent, runtime does not have argument "check_finite"
-scipy.sparse.linalg._isolve._gcrotmk.solve(B is inconsistent, runtime argument "assume_a" has a default value of 'gen', which is different from stub argument default None
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, stub argument "a" has a default value but runtime argument does not
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, stub argument "a" differs from runtime argument "full_matrices"
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "compute_uv"
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, stub argument "full_matrices" differs from runtime argument "overwrite_a"
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "check_finite"
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, stub argument "compute_uv" differs from runtime argument "lapack_driver"
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, runtime argument "lapack_driver" has a default value of type 'gesdd', which is incompatible with stub argument type False. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, runtime does not have argument "overwrite_a"
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, runtime does not have argument "check_finite"
-scipy.sparse.linalg._isolve._gcrotmk.svd(B is inconsistent, runtime does not have argument "lapack_driver"
-scipy.sparse.linalg._isolve.iterative.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.sparse.linalg._isolve.iterative.LinearOperator.dtype(B is not present at runtime
-scipy.sparse.linalg._isolve.lsqr.convert_pydata_sparse_to_scipy(B is not present at runtime
-scipy.sparse.linalg._isolve.utils.LinearOperator.__new__(B is inconsistent, stub does not have *args argument "args"
-scipy.sparse.linalg._isolve.utils.LinearOperator.dtype(B is not present at runtime
-scipy.sparse.linalg._onenormest.sign_round_up(B is inconsistent, stub argument "X" differs from runtime argument "x"
-scipy.sparse.linalg._svdp._svdp(B is not present in stub
-scipy.sparse.linalg.dsolve.MatrixRankWarning(B is not present in stub
-scipy.sparse.linalg.dsolve.SuperLU(B is not present in stub
-scipy.sparse.linalg.dsolve.factorized(B is not present in stub
-scipy.sparse.linalg.dsolve.spilu(B is not present in stub
-scipy.sparse.linalg.dsolve.splu(B is not present in stub
-scipy.sparse.linalg.dsolve.spsolve(B is not present in stub
-scipy.sparse.linalg.dsolve.spsolve_triangular(B is not present in stub
-scipy.sparse.linalg.dsolve.use_solver(B is not present in stub
-scipy.sparse.linalg.eigen.ArpackError(B is not present in stub
-scipy.sparse.linalg.eigen.ArpackNoConvergence(B is not present in stub
-scipy.sparse.linalg.eigen.eigs(B is not present in stub
-scipy.sparse.linalg.eigen.eigsh(B is not present in stub
-scipy.sparse.linalg.eigen.lobpcg(B is not present in stub
-scipy.sparse.linalg.eigen.svds(B is not present in stub
-scipy.sparse.linalg.interface.LinearOperator(B is not present in stub
-scipy.sparse.linalg.interface.aslinearoperator(B is not present in stub
-scipy.sparse.linalg.isolve.bicg(B is not present in stub
-scipy.sparse.linalg.isolve.bicgstab(B is not present in stub
-scipy.sparse.linalg.isolve.cg(B is not present in stub
-scipy.sparse.linalg.isolve.cgs(B is not present in stub
-scipy.sparse.linalg.isolve.gcrotmk(B is not present in stub
-scipy.sparse.linalg.isolve.gmres(B is not present in stub
-scipy.sparse.linalg.isolve.lgmres(B is not present in stub
-scipy.sparse.linalg.isolve.lsmr(B is not present in stub
-scipy.sparse.linalg.isolve.lsqr(B is not present in stub
-scipy.sparse.linalg.isolve.minres(B is not present in stub
-scipy.sparse.linalg.isolve.qmr(B is not present in stub
-scipy.sparse.linalg.isolve.tfqmr(B is not present in stub
-scipy.sparse.linalg.matfuncs.LinearOperator(B is not present in stub
-scipy.sparse.linalg.matfuncs.expm(B is not present in stub
-scipy.sparse.linalg.matfuncs.inv(B is not present in stub
-scipy.sparse.linalg.matfuncs.spsolve(B is not present in stub
-scipy.spatial.Delaunay.find_simplex(B is inconsistent, runtime argument "tol" has a default value of type None, which is incompatible with stub argument type float
-scipy.spatial.KDTree.__init__(B is inconsistent, stub does not have argument "data"
-scipy.spatial.KDTree.__init__(B is inconsistent, stub does not have argument "leafsize"
-scipy.spatial.KDTree.__init__(B is inconsistent, stub does not have argument "compact_nodes"
-scipy.spatial.KDTree.__init__(B is inconsistent, stub does not have argument "copy_data"
-scipy.spatial.KDTree.__init__(B is inconsistent, stub does not have argument "balanced_tree"
-scipy.spatial.KDTree.__init__(B is inconsistent, stub does not have argument "boxsize"
-scipy.spatial.KDTree.query_ball_tree(B is inconsistent, runtime argument "p" has a default value but stub argument does not
-scipy.spatial.cKDTree.__init__(B is inconsistent, stub does not have *args argument "args"
-scipy.spatial.cKDTree.__reduce_cython__(B is not present in stub
-scipy.spatial.cKDTree.__setstate_cython__(B is not present in stub
-scipy.spatial.cKDTree.query_ball_tree(B is inconsistent, runtime argument "p" has a default value but stub argument does not
-scipy.spatial._ckdtree.cKDTree.__init__(B is inconsistent, stub does not have *args argument "args"
-scipy.spatial._ckdtree.cKDTree.__reduce_cython__(B is not present in stub
-scipy.spatial._ckdtree.cKDTree.__setstate_cython__(B is not present in stub
-scipy.spatial._ckdtree.cKDTree.query_ball_tree(B is inconsistent, runtime argument "p" has a default value but stub argument does not
-scipy.spatial._ckdtree.cKDTreeNode.__reduce_cython__(B is not present in stub
-scipy.spatial._ckdtree.cKDTreeNode.__setstate_cython__(B is not present in stub
-scipy.spatial._kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "data"
-scipy.spatial._kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "leafsize"
-scipy.spatial._kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "compact_nodes"
-scipy.spatial._kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "copy_data"
-scipy.spatial._kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "balanced_tree"
-scipy.spatial._kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "boxsize"
-scipy.spatial._kdtree.KDTree.query_ball_tree(B is inconsistent, runtime argument "p" has a default value but stub argument does not
-scipy.spatial._qhull.Delaunay.find_simplex(B is inconsistent, runtime argument "tol" has a default value of type None, which is incompatible with stub argument type float
-scipy.spatial._qhull._Qhull.add_points(B is inconsistent, runtime argument "interior_point" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | bool | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]
-scipy.spatial._qhull._QhullUser.__del__(B is not present in stub
-scipy.spatial._qhull._QhullUser._add_points(B is inconsistent, runtime argument "interior_point" has a default value of type None, which is incompatible with stub argument type numpy._typing._array_like._SupportsArray[numpy.dtype[Any]] | numpy._typing._nested_sequence._NestedSequence[numpy._typing._array_like._SupportsArray[numpy.dtype[Any]]] | bool | int | float | complex | str | bytes | numpy._typing._nested_sequence._NestedSequence[bool | int | float | complex | str | bytes]
-scipy.spatial._spherical_voronoi.cKDTree.__init__(B is inconsistent, stub does not have *args argument "args"
-scipy.spatial._spherical_voronoi.cKDTree.__reduce_cython__(B is not present in stub
-scipy.spatial._spherical_voronoi.cKDTree.__setstate_cython__(B is not present in stub
-scipy.spatial._spherical_voronoi.cKDTree.query_ball_tree(B is inconsistent, runtime argument "p" has a default value but stub argument does not
-scipy.spatial.ckdtree.cKDTree.__init__(B is inconsistent, stub does not have *args argument "args"
-scipy.spatial.ckdtree.cKDTree.__reduce_cython__(B is not present in stub
-scipy.spatial.ckdtree.cKDTree.__setstate_cython__(B is not present in stub
-scipy.spatial.ckdtree.cKDTree.query_ball_tree(B is inconsistent, runtime argument "p" has a default value but stub argument does not
-scipy.spatial.distance.jensenshannon(B is inconsistent, stub does not have argument "axis"
-scipy.spatial.distance.jensenshannon(B is inconsistent, stub does not have argument "keepdims"
-scipy.spatial.distance.kulczynski1(B is inconsistent, stub argument "w" is not keyword-only
-scipy.spatial.kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "data"
-scipy.spatial.kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "leafsize"
-scipy.spatial.kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "compact_nodes"
-scipy.spatial.kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "copy_data"
-scipy.spatial.kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "balanced_tree"
-scipy.spatial.kdtree.KDTree.__init__(B is inconsistent, stub does not have argument "boxsize"
-scipy.spatial.kdtree.KDTree.query_ball_tree(B is inconsistent, runtime argument "p" has a default value but stub argument does not
-scipy.spatial.kdtree.cKDTree.__init__(B is inconsistent, stub does not have *args argument "args"
-scipy.spatial.kdtree.cKDTree.__reduce_cython__(B is not present in stub
-scipy.spatial.kdtree.cKDTree.__setstate_cython__(B is not present in stub
-scipy.spatial.kdtree.cKDTree.query_ball_tree(B is inconsistent, runtime argument "p" has a default value but stub argument does not
-scipy.spatial.qhull.Delaunay.find_simplex(B is inconsistent, runtime argument "tol" has a default value of type None, which is incompatible with stub argument type float
-scipy.spatial.transform.Rotation.__reduce_cython__(B is not present in stub
-scipy.spatial.transform.Rotation.__setstate_cython__(B is not present in stub
-scipy.spatial.transform.Rotation.approx_equal(B is inconsistent, runtime argument "atol" has a default value but stub argument does not
-scipy.spatial.transform._rotation.Rotation.__reduce_cython__(B is not present in stub
-scipy.spatial.transform._rotation.Rotation.__setstate_cython__(B is not present in stub
-scipy.spatial.transform._rotation.Rotation.approx_equal(B is inconsistent, runtime argument "atol" has a default value but stub argument does not
-scipy.spatial.transform.rotation.Rotation.__reduce_cython__(B is not present in stub
-scipy.spatial.transform.rotation.Rotation.__setstate_cython__(B is not present in stub
-scipy.spatial.transform.rotation.Rotation.approx_equal(B is inconsistent, runtime argument "atol" has a default value but stub argument does not
-scipy.special.btdtr(B variable differs from runtime type def (*args: Untyped, **kwargs: Untyped) -> Untyped
-scipy.special.btdtri(B variable differs from runtime type def (*args: Untyped, **kwargs: Untyped) -> Untyped
-scipy.special.comb(B is inconsistent, stub argument "n" differs from runtime argument "N"
-scipy.special.factorial(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.special.factorial2(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.special.factorialk(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.special.perm(B is inconsistent, stub argument "n" differs from runtime argument "N"
-scipy.special.perm(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.special._basic.comb(B is inconsistent, stub argument "n" differs from runtime argument "N"
-scipy.special._basic.factorial(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.special._basic.factorial2(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.special._basic.factorialk(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.special._basic.perm(B is inconsistent, stub argument "n" differs from runtime argument "N"
-scipy.special._basic.perm(B is inconsistent, runtime argument "exact" is not keyword-only
-scipy.special._orthogonal.c_roots(B is not present in stub
-scipy.special._orthogonal.cg_roots(B is not present in stub
-scipy.special._orthogonal.h_roots(B is not present in stub
-scipy.special._orthogonal.he_roots(B is not present in stub
-scipy.special._orthogonal.j_roots(B is not present in stub
-scipy.special._orthogonal.js_roots(B is not present in stub
-scipy.special._orthogonal.l_roots(B is not present in stub
-scipy.special._orthogonal.la_roots(B is not present in stub
-scipy.special._orthogonal.orthopoly1d.__call__(B is inconsistent, stub argument "val" differs from runtime argument "v"
-scipy.special._orthogonal.orthopoly1d.__init__(B is inconsistent, runtime argument "weights" has a default value but stub argument does not
-scipy.special._orthogonal.orthopoly1d.__init__(B is inconsistent, runtime argument "eval_func" has a default value of type None, which is incompatible with stub argument type numpy.ufunc
-scipy.special._orthogonal.orthopoly1d.limits(B is not present at runtime
-scipy.special._orthogonal.orthopoly1d.weight_func(B is not present at runtime
-scipy.special._orthogonal.p_roots(B is not present in stub
-scipy.special._orthogonal.ps_roots(B is not present in stub
-scipy.special._orthogonal.s_roots(B is not present in stub
-scipy.special._orthogonal.t_roots(B is not present in stub
-scipy.special._orthogonal.ts_roots(B is not present in stub
-scipy.special._orthogonal.u_roots(B is not present in stub
-scipy.special._orthogonal.us_roots(B is not present in stub
-scipy.stats.BootstrapMethod.__init__(B is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats.ConfidenceInterval(B is not present at runtime
-scipy.stats.ECDFResult(B is not present at runtime
-scipy.stats.EmpiricalDistributionFunction(B is not present at runtime
-scipy.stats.FitError.__init__(B is inconsistent, stub does not have argument "msg"
-scipy.stats.FitError.__init__(B is inconsistent, runtime does not have *args argument "args"
-scipy.stats.LogRankResult(B is not present at runtime
-scipy.stats.MonteCarloMethod.__init__(B is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats.PermutationMethod.__init__(B is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats.alexandergovern(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.alpha(B is not present in stub
-scipy.stats.anglit(B is not present in stub
-scipy.stats.ansari(B is inconsistent, stub does not have argument "axis"
-scipy.stats.ansari(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.ansari(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.arcsine(B is not present in stub
-scipy.stats.argus(B is not present in stub
-scipy.stats.bartlett(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.bartlett(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.bernoulli(B is not present in stub
-scipy.stats.beta(B is not present in stub
-scipy.stats.betabinom(B is not present in stub
-scipy.stats.betanbinom(B is not present in stub
-scipy.stats.betaprime(B is not present in stub
-scipy.stats.biasedurn(B is not present in stub
-scipy.stats.binom(B is not present in stub
-scipy.stats.boltzmann(B is not present in stub
-scipy.stats.bradford(B is not present in stub
-scipy.stats.brunnermunzel(B is inconsistent, stub does not have argument "axis"
-scipy.stats.brunnermunzel(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.burr(B is not present in stub
-scipy.stats.burr12(B is not present in stub
-scipy.stats.cauchy(B is not present in stub
-scipy.stats.chi(B is not present in stub
-scipy.stats.chi2(B is not present in stub
-scipy.stats.circmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.circstd(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.circvar(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.combine_pvalues(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.combine_pvalues(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.cosine(B is not present in stub
-scipy.stats.cramervonmises(B is inconsistent, stub does not have argument "axis"
-scipy.stats.cramervonmises(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.cramervonmises(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.cramervonmises_2samp(B is inconsistent, stub does not have argument "axis"
-scipy.stats.cramervonmises_2samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.cramervonmises_2samp(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.crystalball(B is not present in stub
-scipy.stats.dgamma(B is not present in stub
-scipy.stats.differential_entropy(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.differential_entropy(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.dlaplace(B is not present in stub
-scipy.stats.dweibull(B is not present in stub
-scipy.stats.entropy(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.entropy(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.epps_singleton_2samp(B is inconsistent, stub does not have argument "axis"
-scipy.stats.epps_singleton_2samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.epps_singleton_2samp(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.erlang(B is not present in stub
-scipy.stats.expon(B is not present in stub
-scipy.stats.exponnorm(B is not present in stub
-scipy.stats.exponpow(B is not present in stub
-scipy.stats.exponweib(B is not present in stub
-scipy.stats.f(B is not present in stub
-scipy.stats.f_oneway(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.f_oneway(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.fatiguelife(B is not present in stub
-scipy.stats.fisk(B is not present in stub
-scipy.stats.fligner(B is inconsistent, stub does not have argument "axis"
-scipy.stats.fligner(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.fligner(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.foldcauchy(B is not present in stub
-scipy.stats.foldnorm(B is not present in stub
-scipy.stats.friedmanchisquare(B is inconsistent, stub does not have argument "axis"
-scipy.stats.friedmanchisquare(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.friedmanchisquare(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.gamma(B is not present in stub
-scipy.stats.gausshyper(B is not present in stub
-scipy.stats.genexpon(B is not present in stub
-scipy.stats.genextreme(B is not present in stub
-scipy.stats.gengamma(B is not present in stub
-scipy.stats.genhalflogistic(B is not present in stub
-scipy.stats.genhyperbolic(B is not present in stub
-scipy.stats.geninvgauss(B is not present in stub
-scipy.stats.genlogistic(B is not present in stub
-scipy.stats.gennorm(B is not present in stub
-scipy.stats.genpareto(B is not present in stub
-scipy.stats.geom(B is not present in stub
-scipy.stats.gibrat(B is not present in stub
-scipy.stats.gmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.gmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.gompertz(B is not present in stub
-scipy.stats.gumbel_l(B is not present in stub
-scipy.stats.gumbel_r(B is not present in stub
-scipy.stats.halfcauchy(B is not present in stub
-scipy.stats.halfgennorm(B is not present in stub
-scipy.stats.halflogistic(B is not present in stub
-scipy.stats.halfnorm(B is not present in stub
-scipy.stats.hmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.hmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.hypergeom(B is not present in stub
-scipy.stats.hypsecant(B is not present in stub
-scipy.stats.invgamma(B is not present in stub
-scipy.stats.invgauss(B is not present in stub
-scipy.stats.invweibull(B is not present in stub
-scipy.stats.irwinhall(B is not present in stub
-scipy.stats.jarque_bera(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.jarque_bera(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.jf_skew_t(B is not present in stub
-scipy.stats.johnsonsb(B is not present in stub
-scipy.stats.johnsonsu(B is not present in stub
-scipy.stats.kappa3(B is not present in stub
-scipy.stats.kappa4(B is not present in stub
-scipy.stats.kruskal(B is inconsistent, stub does not have argument "axis"
-scipy.stats.kruskal(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.ks_1samp(B is inconsistent, stub does not have argument "axis"
-scipy.stats.ks_1samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.ks_1samp(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.ks_2samp(B is inconsistent, stub does not have argument "axis"
-scipy.stats.ks_2samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.ks_2samp(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.ksone(B is not present in stub
-scipy.stats.kstat(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.kstat(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.kstatvar(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.kstatvar(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.kstest(B is inconsistent, stub does not have argument "axis"
-scipy.stats.kstest(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.kstest(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.kstwo(B is not present in stub
-scipy.stats.kstwobign(B is not present in stub
-scipy.stats.kurtosis(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.kurtosistest(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.laplace(B is not present in stub
-scipy.stats.laplace_asymmetric(B is not present in stub
-scipy.stats.levene(B is inconsistent, stub does not have argument "axis"
-scipy.stats.levene(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.levene(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.levy(B is not present in stub
-scipy.stats.levy_l(B is not present in stub
-scipy.stats.loggamma(B is not present in stub
-scipy.stats.logistic(B is not present in stub
-scipy.stats.loglaplace(B is not present in stub
-scipy.stats.lognorm(B is not present in stub
-scipy.stats.logser(B is not present in stub
-scipy.stats.loguniform(B is not present in stub
-scipy.stats.lomax(B is not present in stub
-scipy.stats.mannwhitneyu(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.mannwhitneyu(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.maxwell(B is not present in stub
-scipy.stats.mielke(B is not present in stub
-scipy.stats.moment(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.mood(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.mood(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.moyal(B is not present in stub
-scipy.stats.mvn(B is not present in stub
-scipy.stats.nakagami(B is not present in stub
-scipy.stats.nbinom(B is not present in stub
-scipy.stats.ncf(B is not present in stub
-scipy.stats.nchypergeom_fisher(B is not present in stub
-scipy.stats.nchypergeom_wallenius(B is not present in stub
-scipy.stats.nct(B is not present in stub
-scipy.stats.ncx2(B is not present in stub
-scipy.stats.nhypergeom(B is not present in stub
-scipy.stats.norm(B is not present in stub
-scipy.stats.normaltest(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.norminvgauss(B is not present in stub
-scipy.stats.pareto(B is not present in stub
-scipy.stats.pearson3(B is not present in stub
-scipy.stats.planck(B is not present in stub
-scipy.stats.pmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.pmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.poisson(B is not present in stub
-scipy.stats.powerlaw(B is not present in stub
-scipy.stats.powerlognorm(B is not present in stub
-scipy.stats.powernorm(B is not present in stub
-scipy.stats.randint(B is not present in stub
-scipy.stats.ranksums(B is inconsistent, stub does not have argument "axis"
-scipy.stats.ranksums(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.ranksums(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.rayleigh(B is not present in stub
-scipy.stats.rdist(B is not present in stub
-scipy.stats.recipinvgauss(B is not present in stub
-scipy.stats.reciprocal(B is not present in stub
-scipy.stats.rel_breitwigner(B is not present in stub
-scipy.stats.rice(B is not present in stub
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "momtype" is not keyword-only
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "xtol" is not keyword-only
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats.rv_continuous.__init__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "moment_tol" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, stub does not have argument "values"
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "inc" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats.rv_discrete.__init__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "moment_tol" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "values" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "inc" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats.rv_discrete.__new__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats.rv_discrete.cdf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats.rv_discrete.logcdf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats.rv_discrete.logsf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats.rv_discrete.sf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats.rv_frozen(B is not present at runtime
-scipy.stats.rv_histogram(B is not present in stub
-scipy.stats.rvs_ratio_uniforms(B is not present in stub
-scipy.stats.sampling(B is not present in stub
-scipy.stats.sem(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.semicircular(B is not present in stub
-scipy.stats.shapiro(B is inconsistent, stub does not have argument "axis"
-scipy.stats.shapiro(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.shapiro(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.skellam(B is not present in stub
-scipy.stats.skew(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.skewcauchy(B is not present in stub
-scipy.stats.skewnorm(B is not present in stub
-scipy.stats.skewtest(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.studentized_range(B is not present in stub
-scipy.stats.t(B is not present in stub
-scipy.stats.tmax(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.tmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.tmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.tmin(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.trapezoid(B is not present in stub
-scipy.stats.trapz(B is not present in stub
-scipy.stats.triang(B is not present in stub
-scipy.stats.truncexpon(B is not present in stub
-scipy.stats.truncnorm(B is not present in stub
-scipy.stats.truncpareto(B is not present in stub
-scipy.stats.truncweibull_min(B is not present in stub
-scipy.stats.tsem(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.tsem(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.tstd(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.tstd(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.ttest_1samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.ttest_ind(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.ttest_rel(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.tukeylambda(B is not present in stub
-scipy.stats.tvar(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.tvar(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.uniform(B is not present in stub
-scipy.stats.vonmises(B is not present in stub
-scipy.stats.vonmises_line(B is not present in stub
-scipy.stats.wald(B is not present in stub
-scipy.stats.weibull_max(B is not present in stub
-scipy.stats.weibull_min(B is not present in stub
-scipy.stats.wilcoxon(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.wilcoxon(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.wrapcauchy(B is not present in stub
-scipy.stats.yulesimon(B is not present in stub
-scipy.stats.zipf(B is not present in stub
-scipy.stats.zipfian(B is not present in stub
-scipy.stats._axis_nan_policy.too_small_1d_not_omit(B is not present in stub
-scipy.stats._axis_nan_policy.too_small_1d_omit(B is not present in stub
-scipy.stats._axis_nan_policy.too_small_nd_not_omit(B is not present in stub
-scipy.stats._axis_nan_policy.too_small_nd_omit(B is not present in stub
-scipy.stats._continuous_distns.norm_gen.fit(B is inconsistent, runtime does not have *args argument "args"
-scipy.stats._continuous_distns.reciprocal_gen.fit_note(B is not present in stub
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, stub does not have argument "histogram"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, stub does not have *args argument "args"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "a"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "b"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "badvalue"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "longname"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "momtype"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "name"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "seed"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "shapes"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, runtime does not have argument "xtol"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, stub does not have argument "density"
-scipy.stats._continuous_distns.rv_histogram.__init__(B is inconsistent, stub does not have **kwargs argument "kwargs"
-scipy.stats._discrete_distns._nchypergeom_gen.dist(B is not present in stub
-scipy.stats._discrete_distns._nchypergeom_gen.rvs_name(B is not present in stub
-scipy.stats._discrete_distns.nchypergeom_fisher_gen.dist(B is not present in stub
-scipy.stats._discrete_distns.nchypergeom_fisher_gen.rvs_name(B is not present in stub
-scipy.stats._discrete_distns.nchypergeom_wallenius_gen.dist(B is not present in stub
-scipy.stats._discrete_distns.nchypergeom_wallenius_gen.rvs_name(B is not present in stub
-scipy.stats._distn_infrastructure.docdict(B is not present in stub
-scipy.stats._distn_infrastructure.docdict_discrete(B is not present in stub
-scipy.stats._distn_infrastructure.docheaders(B is not present in stub
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "momtype" is not keyword-only
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "xtol" is not keyword-only
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats._distn_infrastructure.rv_continuous.__init__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "moment_tol" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, stub does not have argument "values"
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "inc" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__init__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "moment_tol" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "values" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "inc" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.__new__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats._distn_infrastructure.rv_discrete.cdf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats._distn_infrastructure.rv_discrete.logcdf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats._distn_infrastructure.rv_discrete.logsf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats._distn_infrastructure.rv_discrete.sf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats._distn_infrastructure.rv_frozen.dist(B is not present at runtime
-scipy.stats._distn_infrastructure.rv_frozen.moment(B is inconsistent, runtime argument "order" has a default value but stub argument does not
-scipy.stats._distn_infrastructure.rv_generic._nnlf_and_penalty(B is not present at runtime
-scipy.stats._distn_infrastructure.rv_generic._unpack_loc_scale(B is not present at runtime
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, stub does not have argument "a"
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, stub does not have argument "b"
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, runtime argument "moment_tol" is not keyword-only
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, runtime argument "values" is not keyword-only
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, runtime argument "inc" is not keyword-only
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, stub does not have argument "shapes"
-scipy.stats._distn_infrastructure.rv_sample.__init__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats._distn_infrastructure.rv_sample.a(B is not present at runtime
-scipy.stats._distn_infrastructure.rv_sample.b(B is not present at runtime
-scipy.stats._distn_infrastructure.rv_sample.generic_moment(B is inconsistent, runtime does not have *args argument "args"
-scipy.stats._distn_infrastructure.rv_sample.pk(B is not present at runtime
-scipy.stats._distn_infrastructure.rv_sample.qvals(B is not present at runtime
-scipy.stats._distn_infrastructure.rv_sample.vecentropy(B is not present at runtime
-scipy.stats._distn_infrastructure.rv_sample.xk(B is not present at runtime
-scipy.stats._entropy.differential_entropy(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._entropy.differential_entropy(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._entropy.entropy(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._entropy.entropy(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._hypotests.cramervonmises(B is inconsistent, stub does not have argument "axis"
-scipy.stats._hypotests.cramervonmises(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._hypotests.cramervonmises(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._hypotests.cramervonmises_2samp(B is inconsistent, stub does not have argument "axis"
-scipy.stats._hypotests.cramervonmises_2samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._hypotests.cramervonmises_2samp(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._hypotests.epps_singleton_2samp(B is inconsistent, stub does not have argument "axis"
-scipy.stats._hypotests.epps_singleton_2samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._hypotests.epps_singleton_2samp(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._mannwhitneyu.mannwhitneyu(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._mannwhitneyu.mannwhitneyu(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.ansari(B is inconsistent, stub does not have argument "axis"
-scipy.stats._morestats.ansari(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.ansari(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.bartlett(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.bartlett(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.circmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.circstd(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.circvar(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.fligner(B is inconsistent, stub does not have argument "axis"
-scipy.stats._morestats.fligner(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.fligner(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.kstat(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.kstat(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.kstatvar(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.kstatvar(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.levene(B is inconsistent, stub does not have argument "axis"
-scipy.stats._morestats.levene(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.levene(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.mood(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.mood(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.shapiro(B is inconsistent, stub does not have argument "axis"
-scipy.stats._morestats.shapiro(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.shapiro(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._morestats.wilcoxon(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._morestats.wilcoxon(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._mstats_basic.mquantiles(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats._mstats_extras.hdquantiles(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats._mstats_extras.hdquantiles_sd(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats._mstats_extras.mjci(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats._mstats_extras.mquantiles_cimj(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats._multicomp.DecimalNumber(B is not present at runtime
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_alternative"
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_rho"
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_df"
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_std"
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_mean_samples"
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_mean_control"
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_n_samples"
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_n_control"
-scipy.stats._multicomp.DunnettResult.__init__(B is inconsistent, stub does not have argument "_rng"
-scipy.stats._multicomp.DunnettResult.__match_args__(B variable differs from runtime type ('statistic', 'pvalue', '_alternative', '_rho', '_df', '_std', '_mean_samples', '_mean_control', '_n_samples', '_n_control', '_rng', '_ci', '_ci_cl')
-scipy.stats._multicomp.SeedType(B is not present at runtime
-scipy.stats._multivariate.matrix_normal_frozen.logpdf(B is inconsistent, stub argument "x" differs from runtime argument "X"
-scipy.stats._multivariate.matrix_normal_frozen.pdf(B is inconsistent, stub argument "x" differs from runtime argument "X"
-scipy.stats._multivariate.multi_rv_frozen._dist(B is not present at runtime
-scipy.stats._multivariate.multi_rv_generic.__call__(B is not a function
-scipy.stats._multivariate.multivariate_normal_frozen.mean(B is not present at runtime
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, stub argument "loc" differs from runtime argument "shape"
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, runtime argument "shape" has a default value of type 1, which is incompatible with stub argument type typing.Sequence[float | numpy.integer[Any] | numpy.floating[Any]] | optype.numpy._array.CanArray[(int,), numpy.dtype[numpy.integer[Any] | numpy.floating[Any]]] | numpy.ndarray[Any, numpy.dtype[numpy.integer[Any] | numpy.floating[Any]]] | None. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, stub argument "shape" differs from runtime argument "df"
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, stub argument "shape" differs from runtime argument "size"
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, stub argument "df" differs from runtime argument "random_state"
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, runtime argument "random_state" has a default value of type None, which is incompatible with stub argument type int. This is often caused by overloads failing to account for explicitly passing in the default value.
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, runtime does not have argument "df"
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, runtime does not have argument "size"
-scipy.stats._multivariate.multivariate_t_gen.rvs(B is inconsistent, runtime does not have argument "random_state"
-scipy.stats._multivariate.uniform_direction_frozen.rvs(B is inconsistent, runtime argument "size" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats._multivariate.uniform_direction_frozen.rvs(B is inconsistent, runtime argument "size" has a default value of None, which is different from stub argument default 1
-scipy.stats._multivariate.uniform_direction_gen.rvs(B is inconsistent, runtime argument "size" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats._multivariate.uniform_direction_gen.rvs(B is inconsistent, runtime argument "size" has a default value of None, which is different from stub argument default 1
-scipy.stats._qmc.PoissonDisk.__init__(B is inconsistent, runtime does not have argument "l_bounds"
-scipy.stats._qmc.PoissonDisk.__init__(B is inconsistent, runtime does not have argument "u_bounds"
-scipy.stats._qmc_cy.DecimalNumber(B is not present at runtime
-scipy.stats._qmc_cy.IntNumber(B is not present at runtime
-scipy.stats._resampling.BootstrapMethod.__init__(B is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats._resampling.MonteCarloMethod.__init__(B is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats._resampling.PermutationMethod.__init__(B is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats._resampling.ResamplingMethod.__init__(B is inconsistent, runtime argument "batch" has a default value of type None, which is incompatible with stub argument type int
-scipy.stats._resampling.ResamplingMethod.batch(B variable differs from runtime type None
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_alternative"
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_rho"
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_df"
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_std"
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_mean_samples"
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_mean_control"
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_n_samples"
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_n_control"
-scipy.stats._result_classes.DunnettResult.__init__(B is inconsistent, stub does not have argument "_rng"
-scipy.stats._result_classes.DunnettResult.__match_args__(B variable differs from runtime type ('statistic', 'pvalue', '_alternative', '_rho', '_df', '_std', '_mean_samples', '_mean_control', '_n_samples', '_n_control', '_rng', '_ci', '_ci_cl')
-scipy.stats._result_classes.EmpiricalDistributionFunction.__match_args__(B variable differs from runtime type ('quantiles', 'probabilities', '_n', '_d', '_sf', '_kind')
-scipy.stats._sensitivity_analysis.SobolResult.__init__(B is inconsistent, stub does not have argument "_indices_method"
-scipy.stats._sensitivity_analysis.SobolResult.__init__(B is inconsistent, stub does not have argument "_f_A"
-scipy.stats._sensitivity_analysis.SobolResult.__init__(B is inconsistent, stub does not have argument "_f_B"
-scipy.stats._sensitivity_analysis.SobolResult.__init__(B is inconsistent, stub does not have argument "_f_AB"
-scipy.stats._sensitivity_analysis.SobolResult.__match_args__(B variable differs from runtime type ('first_order', 'total_order', '_indices_method', '_f_A', '_f_B', '_f_AB', '_A', '_B', '_AB', '_bootstrap_result')
-scipy.stats._sobol.IntNumber(B is not present at runtime
-scipy.stats._sobol.get_poly_vinit(B is not present in stub
-scipy.stats._stats_mstats_common._find_repeats(B is not present in stub
-scipy.stats._stats_py.PearsonRResultBase.__getnewargs_ex__(B is not present in stub
-scipy.stats._stats_py.PearsonRResultBase.__init__(B is inconsistent, stub does not have argument "statistic"
-scipy.stats._stats_py.PearsonRResultBase.__init__(B is inconsistent, stub does not have argument "pvalue"
-scipy.stats._stats_py.PearsonRResultBase._make(B is not present at runtime
-scipy.stats._stats_py.PearsonRResultBase._replace(B is not present at runtime
-scipy.stats._stats_py.QuantileTestResult.__init__(B is inconsistent, stub does not have argument "_alternative"
-scipy.stats._stats_py.QuantileTestResult.__init__(B is inconsistent, stub does not have argument "_x"
-scipy.stats._stats_py.QuantileTestResult.__init__(B is inconsistent, stub does not have argument "_p"
-scipy.stats._stats_py.QuantileTestResult.__match_args__(B variable differs from runtime type ('statistic', 'statistic_type', 'pvalue', '_alternative', '_x', '_p')
-scipy.stats._stats_py.TtestResultBase.__getnewargs_ex__(B is not present in stub
-scipy.stats._stats_py.TtestResultBase.__init__(B is inconsistent, stub does not have argument "statistic"
-scipy.stats._stats_py.TtestResultBase.__init__(B is inconsistent, stub does not have argument "pvalue"
-scipy.stats._stats_py.TtestResultBase._make(B is not present at runtime
-scipy.stats._stats_py.TtestResultBase._replace(B is not present at runtime
-scipy.stats._stats_py._SimpleChi2.cdf(B is not present at runtime
-scipy.stats._stats_py._SimpleNormal.isf(B is not present at runtime
-scipy.stats._stats_py.alexandergovern(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.brunnermunzel(B is inconsistent, stub does not have argument "axis"
-scipy.stats._stats_py.brunnermunzel(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.combine_pvalues(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.combine_pvalues(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.f_oneway(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.f_oneway(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.friedmanchisquare(B is inconsistent, stub does not have argument "axis"
-scipy.stats._stats_py.friedmanchisquare(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.friedmanchisquare(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.gmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.gmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.hmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.hmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.jarque_bera(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.jarque_bera(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.kruskal(B is inconsistent, stub does not have argument "axis"
-scipy.stats._stats_py.kruskal(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.ks_1samp(B is inconsistent, stub does not have argument "axis"
-scipy.stats._stats_py.ks_1samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.ks_1samp(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.ks_2samp(B is inconsistent, stub does not have argument "axis"
-scipy.stats._stats_py.ks_2samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.ks_2samp(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.kstest(B is inconsistent, stub does not have argument "axis"
-scipy.stats._stats_py.kstest(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.kstest(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.kurtosis(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.kurtosistest(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.moment(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.normaltest(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.pmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.pmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.ranksums(B is inconsistent, stub does not have argument "axis"
-scipy.stats._stats_py.ranksums(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.ranksums(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.sem(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.skew(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.skewtest(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.tmax(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.tmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.tmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.tmin(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.tsem(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.tsem(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.tstd(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.tstd(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_py.ttest_1samp(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.ttest_ind(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.ttest_rel(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.tvar(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats._stats_py.tvar(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats._stats_pythran.__pythran__(B is not present in stub
-scipy.stats._survival.EmpiricalDistributionFunction.__match_args__(B variable differs from runtime type ('quantiles', 'probabilities', '_n', '_d', '_sf', '_kind')
-scipy.stats._warnings_errors.FitError.__init__(B is inconsistent, stub does not have argument "msg"
-scipy.stats._warnings_errors.FitError.__init__(B is inconsistent, runtime does not have *args argument "args"
-scipy.stats.distributions.entropy(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.distributions.entropy(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "momtype" is not keyword-only
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "xtol" is not keyword-only
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats.distributions.rv_continuous.__init__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "moment_tol" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, stub does not have argument "values"
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "inc" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats.distributions.rv_discrete.__init__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "a" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "b" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "name" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "badvalue" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "moment_tol" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "values" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "inc" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "longname" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "shapes" is not keyword-only
-scipy.stats.distributions.rv_discrete.__new__(B is inconsistent, runtime argument "seed" is not keyword-only
-scipy.stats.distributions.rv_discrete.cdf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats.distributions.rv_discrete.logcdf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats.distributions.rv_discrete.logsf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats.distributions.rv_discrete.sf(B is inconsistent, stub argument "x" differs from runtime argument "k"
-scipy.stats.distributions.rv_frozen.dist(B is not present at runtime
-scipy.stats.distributions.rv_frozen.moment(B is inconsistent, runtime argument "order" has a default value but stub argument does not
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, stub does not have argument "histogram"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, stub does not have *args argument "args"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "a"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "b"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "badvalue"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "longname"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "momtype"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "name"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "seed"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "shapes"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, runtime does not have argument "xtol"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, stub does not have argument "density"
-scipy.stats.distributions.rv_histogram.__init__(B is inconsistent, stub does not have **kwargs argument "kwargs"
-scipy.stats.mstats.BrunnerMunzelResult(B is not present at runtime
-scipy.stats.mstats.DescribeResult(B is not present at runtime
-scipy.stats.mstats.F_onewayResult(B is not present at runtime
-scipy.stats.mstats.FriedmanchisquareResult(B is not present at runtime
-scipy.stats.mstats.KruskalResult(B is not present at runtime
-scipy.stats.mstats.KurtosistestResult(B is not present at runtime
-scipy.stats.mstats.MannwhitneyuResult(B is not present at runtime
-scipy.stats.mstats.ModeResult(B is not present at runtime
-scipy.stats.mstats.NormaltestResult(B is not present at runtime
-scipy.stats.mstats.PointbiserialrResult(B is not present at runtime
-scipy.stats.mstats.SenSeasonalSlopesResult(B is not present at runtime
-scipy.stats.mstats.SkewtestResult(B is not present at runtime
-scipy.stats.mstats.Ttest_1sampResult(B is not present at runtime
-scipy.stats.mstats.Ttest_indResult(B is not present at runtime
-scipy.stats.mstats.Ttest_relResult(B is not present at runtime
-scipy.stats.mstats.beta(B is not present at runtime
-scipy.stats.mstats.binom(B is not present at runtime
-scipy.stats.mstats.gmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.mstats.gmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.mstats.hdquantiles(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats.mstats.hdquantiles_sd(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats.mstats.hmean(B is inconsistent, stub does not have argument "keepdims"
-scipy.stats.mstats.hmean(B is inconsistent, stub does not have argument "nan_policy"
-scipy.stats.mstats.mjci(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats.mstats.mquantiles(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats.mstats.mquantiles_cimj(B is inconsistent, runtime argument "prob" has a default value of [0.25, 0.5, 0.75], which is different from stub argument default (0.25, 0.5, 0.75)
-scipy.stats.mstats.norm(B is not present at runtime
-scipy.stats.mstats.stde_median(B is not present at runtime
-scipy.stats.mstats.t(B is not present at runtime
-scipy.stats.mstats.trim1(B is not present at runtime
-scipy.stats.mstats.trimdoc(B is not present at runtime
-scipy.stats.qmc.PoissonDisk.__init__(B is inconsistent, runtime does not have argument "l_bounds"
-scipy.stats.qmc.PoissonDisk.__init__(B is inconsistent, runtime does not have argument "u_bounds"
-scipy.stats.sampling.DiscreteAliasUrn(B is not present in stub
-scipy.stats.sampling.DiscreteGuideTable(B is not present in stub
-scipy.stats.sampling.NumericalInverseHermite(B is not present in stub
-scipy.stats.sampling.NumericalInversePolynomial(B is not present in stub
-scipy.stats.sampling.SimpleRatioUniforms(B is not present in stub
-scipy.stats.sampling.TransformedDensityRejection(B is not present in stub
-scipy.stats.sampling.UNURANError(B is not present in stub
-scipy.fftpack.convolve(B failed to find stubs
-scipy.linalg.cython_blas(B failed to find stubs
-scipy.linalg.cython_lapack(B failed to find stubs
-scipy.optimize._lsq.givens_elimination(B failed to find stubs
-scipy.stats._levy_stable.levyst(B failed to find stubs
-scipy.version(B failed to find stubs