diff --git a/torch/onnx/utils.py b/torch/onnx/utils.py
index 37d7ee4b35a73..4e3510472cd96 100644
--- a/torch/onnx/utils.py
+++ b/torch/onnx/utils.py
@@ -813,64 +813,6 @@ def _decide_input_format(model, args):
     return args
 
 
-def _from_dynamic_axes_to_dynamic_shapes(
-    model,
-    dynamic_axes: Mapping[str, Mapping[int, str]]
-    | Mapping[str, Sequence[int]]
-    | None = None,
-    input_names: Sequence[str] | None = None,
-) -> dict[str, Any] | None:
-    """
-
-    dynamic_axes examples:
-    (1) dynamic_axes = {"x": {0: "my_custom_axis_name_1"}, "y": {1: "my_custom_axis_name_2"}}
-    (2) dynamic_axes = {"x": [0], "y": [1]}
-
-    these will be converted to dynamic_shapes respectively:
-    (1) dynamic_shapes = {"x": {0: Dim("my_custom_axis_name_1")}, "y": {1: Dim("my_custom_axis_name_2")}}
-    (2) dynamic_shapes = {"x": {0: Dim("x_dim_0")}, "y": {1: Dim("y_dim_1")}}  # auto-generated dim names
-
-    """
-    if dynamic_axes is None:
-        return None
-
-    if input_names is None:
-        input_names_set = set()
-    else:
-        input_names_set = set(input_names)
-
-    dynamic_shapes: dict[str, Any | None] = {}
-    for input_name, axes in dynamic_axes.items():
-        if input_name in input_names_set:
-            raise ValueError(
-                "Assinging new input names is not supported yet. Please use model forward signature "
-                "to specify input names in dynamix_axes."
-            )
-        if isinstance(axes, dict):
-            dynamic_shapes[input_name] = {
-                k: torch.export.Dim(v) for k, v in axes.items()
-            }
-        elif isinstance(axes, list):
-            dynamic_shapes[input_name] = {
-                k: torch.export.Dim(f"{input_name}_dim_{k}") for k in axes
-            }
-        else:
-            raise TypeError(
-                f"dynamic_axes value must be either a dict or a list, but got {type(axes)}"
-            )
-    # torch.export.export needs static dim to present in dynamic_shapes
-    # for all input tensors, so we need to add them with None
-    try:
-        sig = _signature(model)
-    except ValueError as e:
-        warnings.warn(f"{e}, skipping auto filling None on static axes...")
-        return dynamic_shapes
-    for input_name in sig.parameters.keys():
-        if input_name not in dynamic_shapes:
-            dynamic_shapes[input_name] = None
-    return dynamic_shapes
-
-
 def _trace(func, args, operator_export_type, return_outs=False):
     # Special case for common case of passing a single Tensor
     if isinstance(args, torch.Tensor):