diff --git a/python/tvm/relay/testing/py_converter.py b/python/tvm/relay/testing/py_converter.py
index 1ec85faea619..44489aa9cf7d 100644
--- a/python/tvm/relay/testing/py_converter.py
+++ b/python/tvm/relay/testing/py_converter.py
@@ -38,7 +38,7 @@
 #     import tvm
 #     from tvm import relay
 #     from tvm import nd
-#     from tvm.runtime import import container as _container
+#     from tvm.runtime import container as _container
 #     from tvm.relay.backend.interpreter import RefValue, ConstructorValue
 PROLOGUE = [
     ast.Import([alias("numpy", None)]),
@@ -60,7 +60,7 @@ class PythonConverter(ExprFunctor):
     def __init__(self, mod, target) -> None:
         super().__init__()
         self.mod = mod
-        self.tgt = target
+        self.tgt = target if isinstance(target, tvm.target.Target) else tvm.target.Target(target)
         self.tec = te_compiler.get()
         self.fun_no = 0
         self.var_no = 0
@@ -98,15 +98,31 @@ def optimize(self, prog: Expr):
         # unwrap tuple wrappers (some op calls produce them)
         unwrapped = prog.astuple() if isinstance(prog, relay.TupleWrapper) else prog
         assert relay.analysis.well_formed(unwrapped)
-        mod = self.mod.from_expr(unwrapped, self.mod.functions, self.mod.type_definitions)
+        # For a lone global var, there is nothing we need to do
+        if isinstance(unwrapped, relay.GlobalVar):
+            return unwrapped
+
+        # main might be in the mod already and from_expr will not override it if it's there,
+        # so we need a new name
+        target_name = self.generate_function_name("target")
+
+        wrapped = unwrapped
+        if not isinstance(unwrapped, relay.Function):
+            wrapped = relay.Function(relay.analysis.free_vars(unwrapped), unwrapped)
+
+        # easiest way to make a deep copy -- note that main will not be overridden if it's present
+        copy_mod = tvm.IRModule.from_expr(
+            relay.Tuple([]), self.mod.functions, self.mod.type_definitions
+        )
+        copy_mod[target_name] = wrapped
 
         # necessary pass: SimplifyInference (otherwise we can't generate code for some operators)
         # and fusion (to get primitive functions)
         opts = tvm.transform.Sequential(
             [relay.transform.SimplifyInference(), relay.transform.FuseOps(fuse_opt_level=0)]
         )
-        mod = opts(mod)
-        optimized = mod["main"]
+        copy_mod = opts(copy_mod)
+        optimized = copy_mod[target_name]
         return optimized if isinstance(unwrapped, Function) else optimized.body
 
     def sanitize(self, name: str) -> str:
@@ -197,7 +213,7 @@ def convert_func_node(self, func: Function, name_var=None):
 
         var_names = [self.get_var_name(var) for var in func.params]
         body, defs = self.visit(func.body)
-        ret = self.create_def(func_name, var_names, defs + [Return(body)])
+        ret = self.create_def(func_name, var_names, defs + [Return(body)], register_packed=True)
         return (ret, func_name)
 
     def convert_module(self):
@@ -219,10 +235,25 @@ def create_call(self, func_name: str, arguments):
         """Creates a simple function call."""
         return ast.Call(self.parse_name(func_name), arguments, [])
 
-    def create_def(self, func_name: str, arguments: [str], body):
-        """Wrapper over function definition AST node, whose constructor is inconvenient."""
+    def create_def(self, func_name: str, arguments: [str], body, register_packed: bool = False):
+        """
+        Wrapper over function definition AST node, whose constructor is inconvenient.
+
+        register_packed includes a tvm.register_func decorator on the generated function if true.
+        This option should be used for Relay functions (warning: clobbers registry!)
+        """
         inner_args = [ast.arg(argument, None) for argument in arguments]
 
+        # add a decorator to register as a PackedFunc so the function will be an ObjectRef
+        # and will allow for putting functions into tuples or refs
+        decorator_list = [
+            ast.Call(
+                self.parse_name("tvm.register_func"),
+                [ast.Constant(value=func_name)],
+                [ast.keyword(arg="override", value=ast.Constant(value=True))],
+            )
+        ]
+
         global __MAJOR__, __MINOR__
         if __MAJOR__ == 3 and __MINOR__ >= 8:
             arguments = ast.arguments([], inner_args, None, [], [], None, [])
@@ -233,10 +264,19 @@ def create_def(self, func_name: str, arguments: [str], body):
             func_name,
             arguments,
             body,
-            [],
+            decorator_list if register_packed else [],
             None,
         )
 
+    def create_tuple(self, fields):
+        """
+        Given the ASTs for tuple fields, produce an AST that creates a
+        tuple value with those fields
+        """
+        # Use the FFI API directly so that PackedFuncs will be correctly converted to ObjectRef.
+        # Using tvm.runtime.container.tuple_object fails to convert PackedFuncs in Python
+        return self.create_call("_container._ffi_api.Tuple", fields)
+
     def create_op_call(self, op: Function, relay_args, py_args):
         """Lowers the passed primitive function, registers it in TVM's
         global compiler, and produces a call to the lowered function in
@@ -290,8 +330,7 @@ def convert_output(ret_type):
                 assignments += inner_assignments
                 extra_args += inner_args
                 fields.append(inner_output)
-            fields = [ast.List(fields, Load())]
-            return (assignments, extra_args, self.create_call("_container.tuple_object", fields))
+            return (assignments, extra_args, self.create_tuple(fields))
 
         # create a function to wrap the call of the lowered op and return
         # a call to that function
@@ -418,7 +457,9 @@ def visit_var(self, var: Expr):
     def visit_global_var(self, gvar: Expr):
         # we don't need to add numbers to global var names because
         # the *names* are checked for uniqueness in the mod
-        return (Name(str(gvar.name_hint), Load()), [])
+        func_name = str(gvar.name_hint)
+        # load in the packed func
+        return (self.create_call("tvm.get_global_func", [ast.Constant(value=func_name)]), [])
 
     def visit_let(self, letexp: Expr):
         # To properly account for scoping and ensure that the entire node produces an expression,
@@ -456,8 +497,7 @@ def let_thunk(var):
 
     def visit_tuple(self, tup: Expr):
         fields, ret_defs = self.convert_fields(tup.fields)
-        fields = [ast.List(fields, Load())]
-        return (self.create_call("_container.tuple_object", fields), ret_defs)
+        return (self.create_tuple(fields), ret_defs)
 
     def visit_tuple_getitem(self, tgi: Expr):
         tup, tup_defs = self.visit(tgi.tuple_value)
@@ -471,7 +511,7 @@ def visit_if(self, if_block: Expr):
 
         # need to get the value out of a NDArray to check the condition
         # equvialent to: val.numpy()
-        cond_check = ast.Call(ast.Attribute(cond_body, "asnumpy", Load()), [], [])
+        cond_check = ast.Call(ast.Attribute(cond_body, "numpy", Load()), [], [])
         ret = ast.IfExp(cond_check, true_body, false_body)
         return (ret, cond_defs + true_defs + false_defs)
 
@@ -490,7 +530,11 @@ def visit_constant(self, constant: Expr):
     def visit_function(self, func: Expr):
         # Python's lambdas are very restrictive, so we do "name" inline functions
         converted_func, func_name = self.convert_func_node(func)
-        return (Name(func_name, Load()), [converted_func])
+        # load in the PackedFunc
+        return (
+            self.create_call("tvm.get_global_func", [ast.Constant(value=func_name)]),
+            [converted_func],
+        )
 
     def visit_call(self, call: Expr):
         """For calls, we must distinguish between ordinary functions,
@@ -546,7 +590,7 @@ def visit_ref_write(self, write: Expr):
             + val_defs
             + [
                 Assign([ast.Attribute(ref, "value", Store())], val),
-                Return(self.create_call("_container.tuple_object", [])),
+                Return(self.create_tuple([])),
             ],
         )
         return (self.create_call(thunk_name, []), [thunk])
@@ -602,7 +646,10 @@ def to_python(expr: Expr, mod=None, target=tvm.target.Target("llvm")):
 
 def run_as_python(expr: Expr, mod=None, target=tvm.target.Target("llvm")):
     """Converts the given Relay expression into a Python script and
-    executes it."""
+    executes it.
+
+    Note that closures will be returned as PackedFuncs
+    """
     mod = mod if mod is not None else tvm.IRModule()
     py_ast = to_python(expr, mod, target)
     code = compile(py_ast, "<string>", "exec")
diff --git a/src/runtime/container.cc b/src/runtime/container.cc
index adcaecbc64cf..7b5105a3fc94 100644
--- a/src/runtime/container.cc
+++ b/src/runtime/container.cc
@@ -202,5 +202,6 @@ TVM_REGISTER_GLOBAL("runtime.GetShapeTupleElem").set_body_typed([](ShapeTuple sh
   ICHECK_LT(idx, shape.size());
   return shape[idx];
 });
+
 }  // namespace runtime
 }  // namespace tvm
diff --git a/tests/python/relay/test_py_converter.py b/tests/python/relay/test_py_converter.py
index bd5635e8cf09..d43ec5861b10 100644
--- a/tests/python/relay/test_py_converter.py
+++ b/tests/python/relay/test_py_converter.py
@@ -18,7 +18,7 @@
 import tvm
 from tvm import te
 from tvm import relay
-from tvm.relay.testing import to_python, run_as_python
+from tvm.relay.testing import run_as_python
 from tvm.relay.prelude import Prelude
 from tvm.runtime.container import ADT
 from tvm.relay.backend.interpreter import RefValue, ConstructorValue
@@ -70,7 +70,6 @@ def test_create_empty_tuple():
 def test_create_scalar():
     scalar = relay.const(1)
     tensor_val = run_as_python(scalar)
-    print(type(tensor_val))
     assert_tensor_value(tensor_val, 1)
 
 
@@ -611,3 +610,65 @@ def reference(x, gamma, beta, moving_mean, moving_var):
     verify_batch_norm([(20, 10), (10,), (10,), (10,), (10,)])
     verify_batch_norm([(10, 50), (50,), (50,), (50,), (50,)])
     verify_batch_norm([(30, 40), (40,), (40,), (40,), (40,)])
+
+
+def test_return_global_var():
+    tt = relay.TensorType([1], "float32")
+    x = relay.Var("x", type_annotation=tt)
+    identity = relay.Function([x], x, ret_type=tt)
+    mod = tvm.IRModule()
+    mod["main"] = identity
+    main_var = mod.get_global_var("main")
+    main_func = run_as_python(main_var, mod=mod)
+
+    arg = tvm.nd.array(np.array([0.0], dtype="float32"))
+    res = main_func(arg)
+    assert arg.numpy() == res.numpy()
+
+
+def test_closure_in_tuple():
+    tt = relay.TensorType([1], "float32")
+    x = relay.Var("x", type_annotation=tt)
+    identity = relay.Function([x], x, ret_type=tt)
+    tup = relay.Tuple([identity, identity])
+    index = relay.TupleGetItem(tup, 0)
+
+    func = run_as_python(index)
+    arg = tvm.nd.array(np.array([0.0], dtype="float32"))
+    res = func(arg)
+    assert arg.numpy() == res.numpy()
+
+
+def test_closure_in_ref():
+    tt = relay.TensorType([1], "float32")
+    x = relay.Var("x", type_annotation=tt)
+    identity = relay.Function([x], x, ret_type=tt)
+    gv = relay.GlobalVar("id")
+
+    r = relay.Var("r")
+    seq = relay.Let(
+        r,
+        relay.RefCreate(gv),
+        relay.Call(relay.RefRead(r), [relay.const(np.array([0.0], dtype="float32"))]),
+    )
+
+    mod = tvm.IRModule()
+    mod[gv] = identity
+    res = run_as_python(seq, mod=mod)
+    assert res.numpy() == np.array([0.0], dtype="float32")
+
+
+def test_compiling_with_main():
+    unit_type = relay.TupleType([])
+    unit = relay.Function([], relay.Tuple([]), ret_type=unit_type)
+
+    x = relay.Var("x", type_annotation=unit_type)
+    identity = relay.Function([x], x, ret_type=unit_type)
+
+    mod = tvm.IRModule()
+    mod["unit"] = unit
+    mod["main"] = identity
+
+    res = run_as_python(mod.get_global_var("main")(mod.get_global_var("unit")()), mod=mod)
+    assert isinstance(res, ADT)
+    assert len(res) == 0