fix pyright errors after ruff linting

sbi/analysis/plot.py

@@ -285,7 +285,7 @@ def pairplot(
     diag: Optional[Union[List[str], str]] = "hist",
     figsize: Tuple = (10, 10),
     labels: Optional[List[str]] = None,
-    ticks: Union[List, torch.Tensor] = None,
+    ticks: Optional[Union[List, torch.Tensor]] = None,
     upper: Optional[str] = None,
     fig=None,
     axes=None,
@@ -470,7 +470,7 @@ def marginal_plot(
     diag: Optional[str] = "hist",
     figsize: Tuple = (10, 10),
     labels: Optional[List[str]] = None,
-    ticks: Union[List, torch.Tensor] = None,
+    ticks: Optional[Union[List, torch.Tensor]] = None,
     fig=None,
     axes=None,
     **kwargs,
@@ -534,7 +534,7 @@ def conditional_marginal_plot(
     resolution: int = 50,
     figsize: Tuple = (10, 10),
     labels: Optional[List[str]] = None,
-    ticks: Union[List, torch.Tensor] = None,
+    ticks: Optional[Union[List, torch.Tensor]] = None,
     fig=None,
     axes=None,
     **kwargs,
@@ -607,7 +607,7 @@ def conditional_pairplot(
     resolution: int = 50,
     figsize: Tuple = (10, 10),
     labels: Optional[List[str]] = None,
-    ticks: Union[List, torch.Tensor] = None,
+    ticks: Optional[Union[List, torch.Tensor]] = None,
     fig=None,
     axes=None,
     **kwargs,

sbi/analysis/tensorboard_output.py

@@ -6,7 +6,7 @@
 import logging
 from copy import deepcopy
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -31,13 +31,13 @@ def plot_summary(
     tags: Optional[List[str]] = None,
     disable_tensorboard_prompt: bool = False,
     tensorboard_scalar_limit: int = 10_000,
-    figsize: List[int] = (20, 6),
+    figsize: Sequence[int] = (20, 6),
     fontsize: float = 12,
     fig: Optional[Figure] = None,
     axes: Optional[Axes] = None,
     xlabel: str = "epochs_trained",
-    ylabel: str = "value",
-    plot_kwargs: Dict[str, Any] = None,
+    ylabel: Optional[List[str]] = None,
+    plot_kwargs: Optional[Dict[str, Any]] = None,
 ) -> Tuple[Figure, Axes]:
     """Plots data logged by the tensorboard summary writer of an inference object.
 

sbi/inference/snpe/snpe_base.py

@@ -505,6 +505,7 @@ def build_posterior(
                 **direct_sampling_parameters or {},
             )
         elif sample_with == "rejection":
+            rejection_sampling_parameters = rejection_sampling_parameters or {}
             if "proposal" not in rejection_sampling_parameters:
                 raise ValueError(
                     "You passed `sample_with='rejection' but you did not specify a "
@@ -517,7 +518,7 @@ def build_posterior(
                 potential_fn=potential_fn,
                 device=device,
                 x_shape=self._x_shape,
-                **rejection_sampling_parameters or {},
+                **rejection_sampling_parameters,
             )
         elif sample_with == "mcmc":
             self._posterior = MCMCPosterior(

sbi/inference/snre/snre_a.py

@@ -60,7 +60,7 @@ def train(
         retrain_from_scratch: bool = False,
         show_train_summary: bool = False,
         dataloader_kwargs: Optional[Dict] = None,
-        loss_kwargs: Dict[str, Any] = None,
+        loss_kwargs: Optional[Dict[str, Any]] = None,
     ) -> nn.Module:
         r"""Return classifier that approximates the ratio $p(\theta,x)/p(\theta)p(x)$.
 

sbi/neural_nets/embedding_nets.py

@@ -108,7 +108,7 @@ def __init__(
         self,
         input_shape: Tuple,
         in_channels: int = 1,
-        out_channels_per_layer: List = (6, 12),
+        out_channels_per_layer: Optional[List] = None,
         num_conv_layers: int = 2,
         num_linear_layers: int = 2,
         num_linear_units: int = 50,
@@ -150,6 +150,8 @@ def __init__(
         conv_module = nn.Conv2d if use_2d_cnn else nn.Conv1d
         pool_module = nn.MaxPool2d if use_2d_cnn else nn.MaxPool1d
 
+        if out_channels_per_layer is None:
+            out_channels_per_layer = [6, 12]
         assert (
             len(out_channels_per_layer) == num_conv_layers
         ), "out_channels needs as many entries as num_cnn_layers."

sbi/utils/metrics.py

@@ -107,7 +107,7 @@ def c2st_scores(
     noise_scale: Optional[float] = None,
     verbosity: int = 0,
     clf_class: Any = RandomForestClassifier,
-    clf_kwargs: Dict[str, Any] = None,
+    clf_kwargs: Optional[Dict[str, Any]] = None,
 ) -> Tensor:
     """
     Return accuracy of classifier trained to distinguish samples from supposedly two

sbi/utils/user_input_checks.py

@@ -39,7 +39,7 @@ def check_prior(prior: Any) -> None:
 
 def process_prior(
     prior: Union[Sequence[Distribution], Distribution, rv_frozen, multi_rv_frozen],
-    custom_prior_wrapper_kwargs: Dict = None,
+    custom_prior_wrapper_kwargs: Optional[Dict] = None,
 ) -> Tuple[Distribution, int, bool]:
     """Return PyTorch distribution-like prior from user-provided prior.