I am done

rl/Lib/site-packages/onnx/reference/ops/op_argmin.py

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+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import numpy as np
+
+from onnx.reference.op_run import OpRun
+
+
+def _argmin(data, axis=0, keepdims=True):  # type: ignore
+    result = np.argmin(data, axis=axis)
+    if keepdims and len(result.shape) < len(data.shape):
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+def _argmin_use_numpy_select_last_index(data, axis=0, keepdims=True):  # type: ignore
+    data = np.flip(data, axis)
+    result = np.argmin(data, axis=axis)
+    result = data.shape[axis] - result - 1
+    if keepdims:
+        result = np.expand_dims(result, axis)
+    return result.astype(np.int64)
+
+
+class _ArgMin(OpRun):
+    def _run(self, data, axis=None, keepdims=None):  # type: ignore
+        return (_argmin(data, axis=axis, keepdims=keepdims),)
+
+
+class ArgMin_1(_ArgMin):
+    pass
+
+
+class ArgMin_12(_ArgMin):
+    def _run(self, data, axis=None, keepdims=None, select_last_index=None):  # type: ignore
+        if select_last_index == 0:  # type: ignore
+            return _ArgMin._run(self, data, axis=axis, keepdims=keepdims)
+        return (
+            _argmin_use_numpy_select_last_index(data, axis=axis, keepdims=keepdims),
+        )