Vulnerability: 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.
Vulnerability ID: GHSA-9vpm-rcf4-9wqw
CWE: CWE-369
Severity score: 2.5
Project: https://github.com/tensorflow/tensorflow
Published: 2021-05-21
File: tensorflow/core/kernels/maxpooling_op.cc
Commit message: Don't do any work if output tensor is null (prevent div by 0)

PiperOrigin-RevId: 372208700
Change-Id: Iea6b6293e887ade8538facfdb50fb931e17f511e
Commit author: Mihai Maruseac

Vulnerable version (before fix):
 class MaxPoolingGradWithArgmaxOp : public OpKernel {     OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(                                 {0}, 0, out_shape, &grad_out));      LaunchMaxPoolingGradWithArgmax<Device, T>::launch(         context, params, grad_in, argmax, grad_out, include_batch_in_index_);   }

Fixed version (after fix):
 class MaxPoolingGradWithArgmaxOp : public OpKernel {     OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(                                 {0}, 0, out_shape, &grad_out));      if (out_shape.num_elements() == 0) return;  // nothing to be done      LaunchMaxPoolingGradWithArgmax<Device, T>::launch(         context, params, grad_in, argmax, grad_out, include_batch_in_index_);   }

Patch:
patch: @@ -1088,6 +1088,8 @@ class MaxPoolingGradWithArgmaxOp : public OpKernel {
     OP_REQUIRES_OK(context, context->forward_input_or_allocate_output(
                                 {0}, 0, out_shape, &grad_out));
 
+    if (out_shape.num_elements() == 0) return;  // nothing to be done
+
     LaunchMaxPoolingGradWithArgmax<Device, T>::launch(
         context, params, grad_in, argmax, grad_out, include_batch_in_index_);
   }
path: tensorflow/core/kernels/maxpooling_op.cc