diff --git a/python/test/compile_api/basic.py b/python/test/compile_api/basic.py
index 5e9d14159..af46f69c5 100644
--- a/python/test/compile_api/basic.py
+++ b/python/test/compile_api/basic.py
@@ -34,6 +34,11 @@ print(torch_mlir.compile(TanhModule(), placeholder))
 # CHECK-LABEL: @forward
 # CHECK: torch.aten.tanh %{{.*}} : !torch.vtensor<[?,2],f32> -> !torch.vtensor<[?,2],f32>
 
+# Basic smoke test for the raw output type.
+print(torch_mlir.compile(TanhModule(), tanh_example_input, output_type=torch_mlir.OutputType.RAW))
+# CHECK: torch.nn_module {
+# CHECK: } : !torch.nn.Module<"__torch__.TanhModule">
+
 class MmModule(torch.nn.Module):
     def __init__(self):
         super().__init__()
diff --git a/python/torch_mlir/__init__.py b/python/torch_mlir/__init__.py
index b7410e1f0..740005f25 100644
--- a/python/torch_mlir/__init__.py
+++ b/python/torch_mlir/__init__.py
@@ -30,6 +30,9 @@ class OutputType(Enum):
     # as taking the `TORCH` output type and lowering it so that tensor
     # computations are done with `linalg`-on-tensors ops.
     LINALG_ON_TENSORS = 2
+    # Raw output of the JIT IR importer. This is not expected to be useful
+    # for end-users, but can be convenient for development or reporting bugs.
+    RAW = 3
 
 
 class TensorPlaceholder:
@@ -140,21 +143,28 @@ def compile(model: torch.nn.Module,
     mb = ModuleBuilder()
     mb.import_module(scripted._c, class_annotator)
 
+    if output_type == OutputType.RAW:
+        return mb.module
+
     run_pipeline_with_repro_report(mb.module,
                                    "torchscript-module-to-torch-backend-pipeline",
                                    "Lowering TorchScript IR -> Torch Backend IR")
 
     if output_type == OutputType.TORCH:
-        pass
-    elif output_type == OutputType.TOSA:
+        return mb.module
+
+    if output_type == OutputType.TOSA:
         run_pipeline_with_repro_report(
             mb.module,
             "torch-backend-to-tosa-backend-pipeline",
             "Lowering Torch Backend IR -> TOSA Backend IR")
-    else:
-        assert output_type == OutputType.LINALG_ON_TENSORS
+        return mb.module
+
+    if output_type == OutputType.LINALG_ON_TENSORS:
         run_pipeline_with_repro_report(
             mb.module,
             "torch-backend-to-linalg-on-tensors-backend-pipeline",
             "Lowering Torch Backend IR -> Linalg-on-Tensors Backend IR")
-    return mb.module
+        return mb.module
+
+    raise Exception(f"Unknown OutputType: {output_type}")