Overflow/crash in `tf.image.resize` when size is large

Impact

If tf.image.resize is called with a large input argument then the TensorFlow process will crash due to a CHECK-failure caused by an overflow.

```python

import tensorflow as tf

import numpy as np

tf.keras.layers.UpSampling2D(

size=1610637938,

data_format='channels_first',

interpolation='bilinear')(np.ones((5,1,1,1)))

`

The number of elements in the output tensor is too much for the int64_t type and the overflow is detected via a CHECK statement. This aborts the process.

Patches

We have patched the issue in GitHub commit [e5272d4204ff5b46136a1ef1204fc00597e21837](https://github.com/tensorflow/tensorflow/commit/e5272d4204ff5b46136a1ef1204fc00597e21837) (merging [#51497](https://github.com/tensorflow/tensorflow/pull/51497)).

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/46914).