diff --git a/frontends/pytorch/csrc/builder/module_builder.cpp b/frontends/pytorch/csrc/builder/module_builder.cpp
index 2114b379b..d941c844a 100644
--- a/frontends/pytorch/csrc/builder/module_builder.cpp
+++ b/frontends/pytorch/csrc/builder/module_builder.cpp
@@ -95,7 +95,8 @@ ModuleBuilder::startCaptureFunction(std::string &name,
   return std::make_shared<AcapController>(typeMapper, std::move(funcBuilder));
 }
 
-void ModuleBuilder::importFunction(torch::jit::StrongFunctionPtr function) {
+torch::jit::StrongFunctionPtr
+ModuleBuilder::importFunction(torch::jit::StrongFunctionPtr function) {
   auto inserter = createInserter();
   GraphImporter::MlirMappingOptions mappingOptions{
       context,
@@ -107,6 +108,7 @@ void ModuleBuilder::importFunction(torch::jit::StrongFunctionPtr function) {
       function.function_, std::move(mappingOptions));
   graphImporter->initialize();
   graphImporter->importGenericFunc();
+  return function;
 }
 
 FuncBuilder::Inserter ModuleBuilder::createInserter() {
diff --git a/frontends/pytorch/csrc/builder/module_builder.h b/frontends/pytorch/csrc/builder/module_builder.h
index 56bbcf935..bff96a2ab 100644
--- a/frontends/pytorch/csrc/builder/module_builder.h
+++ b/frontends/pytorch/csrc/builder/module_builder.h
@@ -40,7 +40,9 @@ public:
   // Imports a traced function. Note that the python type
   // torch.jit.ScriptFunction is the C++ type torch::jit::StrongFunctionPtr.
   // Just a bit of naming cruft.
-  void importFunction(torch::jit::StrongFunctionPtr function);
+  // Returns the same function, making it suitable as a nested decorator.
+  torch::jit::StrongFunctionPtr
+  importFunction(torch::jit::StrongFunctionPtr function);
 
 private:
   FuncBuilder::Inserter createInserter();
diff --git a/frontends/pytorch/test/graph_export/test_errors.py b/frontends/pytorch/test/graph_export/test_errors.py
new file mode 100644
index 000000000..2f817f74e
--- /dev/null
+++ b/frontends/pytorch/test/graph_export/test_errors.py
@@ -0,0 +1,29 @@
+# -*- Python -*-
+# This file is licensed under a pytorch-style license
+# See frontends/pytorch/LICENSE for license information.
+
+import torch
+import torch_mlir
+
+# RUN: %PYTHON %s
+
+@torch.jit.script
+class ExampleClass:
+  def __init__(self, x):
+    self.x = x
+
+
+mb = torch_mlir.ModuleBuilder()
+
+# For now, TorchScript classes are wholly unsupported, so use it to test
+# type conversion errors.
+try:
+  @mb.import_function
+  @torch.jit.script
+  def import_class(c: ExampleClass):
+    return c.x
+except RuntimeError as e:
+  # TODO: Once diagnostics are enabled, verify the actual error emitted.
+  assert str(e) == "could not convert function input type"
+else:
+  assert False, "Expected exception"
diff --git a/frontends/pytorch/test/graph_export/test_script_add3.py b/frontends/pytorch/test/graph_export/test_script_add3.py
index c490df9d3..7d448d4f8 100644
--- a/frontends/pytorch/test/graph_export/test_script_add3.py
+++ b/frontends/pytorch/test/graph_export/test_script_add3.py
@@ -7,12 +7,7 @@ import torch_mlir
 
 # RUN: %PYTHON %s | npcomp-opt | FileCheck %s
 
-@torch.jit.script
-def add3(t0, t1, t2):
-  return t0 + t1 + t2
-
 mb = torch_mlir.ModuleBuilder()
-mb.import_function(add3)
 
 # Verify without debug info.
 # CHECK-LABEL: func @add3$generic
@@ -21,5 +16,11 @@ mb.import_function(add3)
 # CHECK:   %[[A0:.*]] = torch.kernel_call "aten::add" %arg0, %arg1, %[[C1]] : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "Tensor", "Scalar"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
 # CHECK:   %[[A1:.*]] = torch.kernel_call "aten::add" %[[A0]], %arg2, %[[C1]] : (!numpy.ndarray<*:!numpy.any_dtype>, !numpy.ndarray<*:!numpy.any_dtype>, i64) -> !numpy.ndarray<*:!numpy.any_dtype> {sigArgTypes = ["Tensor", "Tensor", "Scalar"], sigIsMutable = false, sigIsVararg = false, sigIsVarret = false, sigRetTypes = ["Tensor"]}
 # CHECK:   return %[[A1]] : !numpy.ndarray<*:!numpy.any_dtype>
+@mb.import_function
+@torch.jit.script
+def add3(t0, t1, t2):
+  return t0 + t1 + t2
+
+assert isinstance(add3, torch.jit.ScriptFunction)
 mb.module.operation.print()
 print()
diff --git a/frontends/pytorch/test/graph_export/test_script_debug_info.py b/frontends/pytorch/test/graph_export/test_script_debug_info.py
index f9a4e2a4e..e44830009 100644
--- a/frontends/pytorch/test/graph_export/test_script_debug_info.py
+++ b/frontends/pytorch/test/graph_export/test_script_debug_info.py
@@ -7,20 +7,25 @@ import torch_mlir
 
 # RUN: %PYTHON %s | FileCheck %s
 
+mb = torch_mlir.ModuleBuilder()
+
+# CHECK-LABEL: func @add3$generic
+# Note that line-level debug information for parts unannotated in the Torch
+# graph are ascribed to the first op that carries source information. Presently
+# this includes naked constants, return and the function itself. This heuristic
+# likely needs to be improved and this test should be reworked when it is.
+@mb.import_function
 @torch.jit.script
 def add3(t0, t1, t2):
+  # CHECK: constant 1{{.*}}loc({{.*}}test_script_debug_info.py":[[# @LINE + 2]]
+  # CHECK: aten::add{{.*}}loc({{.*}}test_script_debug_info.py":[[# @LINE + 1]]
   intermediate = t0 + t1
+  # CHECK: aten::add{{.*}}loc({{.*}}test_script_debug_info.py":[[# @LINE + 1]]
   final = intermediate + t2
+  # CHECK: return{{.*}}loc({{.*}}test_script_debug_info.py":[[# @LINE - 3]]
   return final
-
-mb = torch_mlir.ModuleBuilder()
-mb.import_function(add3)
+  # CHECK: }{{.*}}loc({{.*}}test_script_debug_info.py":[[# @LINE - 5]]
 
 # Verify again with debug info present. Just checking that it makes it in there.
-# CHECK-LABEL: func @add3$generic
-# CHECK: constant 1{{.*}}loc({{.*}}test_script_debug_info.py
-# CHECK: aten::add{{.*}}loc({{.*}}test_script_debug_info.py
-# CHECK: return{{.*}}loc({{.*}}test_script_debug_info.py
-# CHECK: }{{.*}}loc({{.*}}test_script_debug_info.py
 mb.module.operation.print(enable_debug_info=True)
 print()