diff --git a/python/test/eager_mode/annotate_args.py b/python/test/eager_mode/annotate_args.py
new file mode 100644
index 000000000..c933e4960
--- /dev/null
+++ b/python/test/eager_mode/annotate_args.py
@@ -0,0 +1,99 @@
+# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+# Also available under a BSD-style license. See LICENSE.
+
+# RUN: %PYTHON %s | FileCheck %s
+
+
+import torch
+
+from framework import run_test
+from torch_mlir.eager_mode.torch_mlir_dispatch import (
+    annotate_args_kwargs,
+    normalize_args_kwargs,
+    build_script_function,
+)
+
+
+# CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(1, 3, 32, 32), dtype=torch.float32)
+# -----
+# CHECK: PASS - simple
+@run_test
+def simple():
+    target = torch.ops.aten.addmm.default
+    A = torch.randn(1, 3, 32, 32)
+    B = torch.randn(1, 3, 32, 32)
+    C = torch.randn(1, 3, 32, 32)
+    args = (A, B, C)
+    kwargs = dict(beta=1, alpha=1)
+
+    new_args, new_kwargs = normalize_args_kwargs(target.overloadpacket, args, kwargs)
+    script_fun = build_script_function(target._schema, new_args, new_kwargs)
+    annotations, *_ = annotate_args_kwargs(script_fun, new_args, new_kwargs)
+    for annot in annotations:
+        print(annot)
+
+
+# CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(-1, 3, 32, 32), dtype=torch.float32)
+# -----
+# CHECK: PASS - handle_zero_dim
+@run_test
+def handle_zero_dim():
+    target = torch.ops.aten.addmm.default
+    A = torch.randn(0, 3, 32, 32)
+    B = torch.randn(0, 3, 32, 32)
+    C = torch.randn(0, 3, 32, 32)
+    args = (A, B, C)
+    kwargs = dict(beta=1, alpha=1)
+
+    new_args, new_kwargs = normalize_args_kwargs(target.overloadpacket, args, kwargs)
+    script_fun = build_script_function(target._schema, new_args, new_kwargs)
+    annotations, *_ = annotate_args_kwargs(script_fun, new_args, new_kwargs)
+    for annot in annotations:
+        print(annot)
+
+
+# CHECK: Torch Tensor (shape=(2, 5, 2, 3), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(2, 5, 2, 3), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
+# CHECK: Torch Tensor (shape=(5,), dtype=torch.float32)
+# -----
+# CHECK: PASS - correctly_order_kwargs
+@run_test
+def correctly_order_kwargs():
+    target = torch.ops.aten.native_batch_norm.out
+
+    input = torch.randn(2, 5, 2, 3)
+    weight = torch.randn(5)
+    bias = torch.randn(5)
+    running_mean = torch.randn(5)
+    running_var = torch.randn(5)
+    args = (input, weight, bias, running_mean, running_var)
+
+    out = torch.empty_like(input)
+    save_mean = torch.empty_like(running_mean)
+    save_invstd = torch.empty_like(running_var)
+
+    kwargs = dict(
+        training=False,
+        momentum=0.1,
+        eps=0.0001,
+        out=out,
+        save_mean=save_mean,
+        save_invstd=save_invstd,
+    )
+
+    new_args, new_kwargs = normalize_args_kwargs(target.overloadpacket, args, kwargs)
+    script_fun = build_script_function(target._schema, new_args, new_kwargs)
+    annotations, *_ = annotate_args_kwargs(script_fun, new_args, new_kwargs)
+    for annot in annotations:
+        print(annot)
diff --git a/python/torch_mlir/eager_mode/torch_mlir_dispatch.py b/python/torch_mlir/eager_mode/torch_mlir_dispatch.py
index ad2b69f40..ca4ac0828 100644
--- a/python/torch_mlir/eager_mode/torch_mlir_dispatch.py
+++ b/python/torch_mlir/eager_mode/torch_mlir_dispatch.py
@@ -168,8 +168,7 @@ def annotate_args_kwargs(
             if isinstance(arg, np.ndarray):
                 tensor_kwargs[arg_idxs[kw]] = (arg, normalized_kwargs[kw].dtype)
 
-        for i in range(len(tensor_kwargs)):
-            arg, arg_dtype = tensor_kwargs[i]
+        for _i, (arg, arg_dtype) in sorted(tensor_kwargs.items()):
             annotations.append(TorchTensorType(shape=tuple(arg.shape), dtype=arg_dtype))
             tensor_kwargs_flat.append(arg)