I am done

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

@@ -0,0 +1,28 @@
+# Copyright (c) ONNX Project Contributors
+
+# SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
+import math
+
+import numpy as np
+
+from onnx.reference.op_run import OpRun
+
+
+class LRN(OpRun):
+    def _run(self, x, alpha=None, beta=None, bias=None, size=None):  # type: ignore
+        if len(x.shape) != 4:
+            raise RuntimeError(
+                f"LRN only applies on 4D tensors but shape is {x.shape!r}."
+            )
+        square_sum = np.zeros(x.shape).astype(x.dtype)
+        minc = x.shape[1]
+        c1 = int(math.floor((size - 1) / 2))
+        c2 = int(math.ceil((size - 1) / 2)) + 1
+        for c in range(x.shape[0]):
+            begin = max(0, c - c1)
+            end = min(minc, c + c2)
+            square_sum[:, c, :, :] = np.sum(x[:, begin:end, :, :] ** 2, axis=1)
+        y = x / ((bias + (alpha / size) * square_sum) ** beta)
+        return (y.astype(x.dtype),)