Division by 0 in `MaxPoolGradWithArgmax`
### Impact
The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0:
```python
import tensorflow as tf
input = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
grad = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.float32)
argmax = tf.constant([], shape=[0], dtype=tf.int64)
ksize = [1, 1, 1, 1]
strides = [1, 1, 1, 1]
tf.raw_ops.MaxPoolGradWithArgmax(
input=input, grad=grad, argmax=argmax, ksize=ksize, strides=strides,
padding='SAME', include_batch_in_index=False)
```
The implementation
https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034 fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity.
### Patches
We have patched the issue in GitHub commit 376c352a37ce5a68b721406dc7e77ac4b6cf483d
https://github.com/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
### For more information
Please consult our security guide
https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md for more information regarding the security model and how to contact us with issues and questions.
### Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.