diff --git a/projects/pt1/e2e_testing/xfail_sets.py b/projects/pt1/e2e_testing/xfail_sets.py
index 2ae767dde..ab162ab94 100644
--- a/projects/pt1/e2e_testing/xfail_sets.py
+++ b/projects/pt1/e2e_testing/xfail_sets.py
@@ -367,7 +367,6 @@ FX_IMPORTER_XFAIL_SET = {
     "BoolIntTrueModule_basic",
     "BroadcastDynamicDimModule_basic",
     "CeilFloatModule_basic",
-    "ConstantBoolParameterModule_basic",
     "ContainsIntList_False",
     "ContainsIntList_True",
     "Conv2dQInt8Module_basic",
diff --git a/python/torch_mlir/extras/fx_importer.py b/python/torch_mlir/extras/fx_importer.py
index c931a3b93..34d570b55 100644
--- a/python/torch_mlir/extras/fx_importer.py
+++ b/python/torch_mlir/extras/fx_importer.py
@@ -844,6 +844,10 @@ class FxImporter:
                         result_types.append(
                             IrType.parse("!torch.none", context=self._c)
                         )
+                    elif isinstance(result_node, torch.Tensor):
+                        result_types.append(
+                            self._cc.tensor_to_vtensor_type(result_node)
+                        )
                     else:
                         result_types.append(self._cc.node_val_to_type(result_node))
         return (
@@ -1002,9 +1006,14 @@ class ContextCache:
             self._dtype_to_type[dtype] = t
         return t
 
+    def create_vtensor_type(self, dtype: torch.dtype, size: torch.Size) -> IrType:
+        dtype_asm = str(self.dtype_to_type(dtype))
+        return IrType.parse(
+            f"!torch.vtensor<{list(size)},{dtype_asm}>", context=self._c
+        )
+
     def tensor_to_vtensor_type(self, tensor: torch.Tensor) -> IrType:
-        dtype_asm = str(self.dtype_to_type(tensor.dtype))
-        return IrType.parse(f"!torch.vtensor<{list(tensor.size())},{dtype_asm}>")
+        return self.create_vtensor_type(tensor.dtype, tensor.size())
 
     def get_node_location(self, node: torch_fx.Node) -> Optional[Location]:
         stack_trace = node.meta.get("stack_trace")
@@ -1513,37 +1522,58 @@ class GraphNodeImporter:
         ):
             # promote scalars to tensor types as appropriate
             argument_value = self._import_scalar_as_tensor(loc, arg)
-        else:
+        elif LITERAL_CONVERTER_MAP.lookup(type(arg)) is not None:
             with loc:
                 argument_value = self._import_literal(arg)
-        return self._convert_type(loc, argument_value, expected_jit_type)
+        else:
+            raise TypeError(f"Unsupported argument type {arg.__class__}")
+        with loc:
+            return self._convert_type(argument_value, expected_jit_type)
 
-    def _convert_type(self, loc: Location, val: Value, expected_jit_type):
+    def _convert_type(
+        self,
+        val: Value,
+        expected_type,
+        dtype: Optional[torch.dtype] = None,
+        size: Optional[torch.Size] = None,
+    ):
         """
         When the type of 'value' and the type in the schema do not match,
         attempt to perform automatic type conversion.
 
         example: test/python/fx_importer/basic_test.py::test_full
         """
+        if not expected_type:
+            return val
         op_name = None
         result_type = None
         # TODO: If additional types require conversion in the future,
         #  consider implementing a table-driven approach.
+        operands = [val]
         if val.type == self._cc.torch_bool_type:
-            if isinstance(expected_jit_type, torch.FloatType):
+            if isinstance(expected_type, torch.FloatType):
                 op_name = "torch.aten.Float.bool"
                 result_type = self._cc.torch_float_type
-            elif isinstance(expected_jit_type, (torch.IntType, torch.NumberType)):
+            elif isinstance(expected_type, (torch.IntType, torch.NumberType)):
                 op_name = "torch.aten.Int.bool"
                 result_type = self._cc.torch_int_type
+        elif expected_type is torch.Tensor:
+            op_name = "torch.prims.convert_element_type"
+            result_type = self._cc.create_vtensor_type(dtype, size)
+            operands.append(
+                LITERAL_CONVERTER_MAP.lookup(torch.dtype)(dtype, self, self._cc)
+            )
         if op_name is None:
             return val
-        with loc:
-            return Operation.create(
-                name=op_name, results=[result_type], operands=[val]
-            ).result
+        return Operation.create(
+            name=op_name, results=[result_type], operands=operands
+        ).result
 
     def _import_literal(self, py_value: Any) -> Value:
+        orig_value = None
+        if isinstance(py_value, torch.Tensor) and py_value.dtype == torch.bool:
+            orig_value = py_value
+            py_value = py_value.to(torch.uint8)
         # Apply the conversion callback.
         user_value = self.fx_importer._hooks.resolve_literal(self, py_value)
         if user_value is not None:
@@ -1556,7 +1586,12 @@ class GraphNodeImporter:
             raise TypeError(
                 f"Unsupported argument -> literal conversion for {py_value.__class__}"
             )
-        return converter(py_value, self, self._cc)
+        result = converter(py_value, self, self._cc)
+        if orig_value is not None:
+            result = self._convert_type(
+                result, torch.Tensor, orig_value.dtype, orig_value.size()
+            )
+        return result
 
     def _import_input(self, py_value: Any, info: InputInfo) -> Value:
         # Try the hook.
@@ -1704,16 +1739,19 @@ def _make_constant_op(
     )
 
 
-def create_mlir_tensor_type(tensor: torch.Tensor) -> IrType:
+def _create_mlir_tensor_type(dtype: torch.dtype, size: torch.Size) -> IrType:
     try:
-        dtype = tensor.dtype
         element_type = TORCH_DTYPE_TO_MLIR_TYPE[dtype]()
-        tensor_type = RankedTensorType.get(tuple(tensor.size()), element_type)
+        tensor_type = RankedTensorType.get(size, element_type)
         return tensor_type
     except KeyError:
         raise TypeError(f"Could not map Torch dtype {dtype} to an MLIR type")
 
 
+def create_mlir_tensor_type(tensor: torch.Tensor) -> IrType:
+    return _create_mlir_tensor_type(tensor.dtype, tensor.size())
+
+
 def _make_vtensor_literal_op(
     tensor: torch.Tensor, vtensor_type: IrType, py_attr_tracker: "RefTracker"
 ) -> Operation: