Vulnerability: Division by 0 in `DenseCountSparseOutput` ### Impact
An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`:

```python
import tensorflow as tf

values = tf.constant([], shape=[0, 0], dtype=tf.int64)
weights = tf.constant([])

tf.raw_ops.DenseCountSparseOutput(
  values=values, weights=weights,
  minlength=-1, maxlength=58, binary_output=True)
```
  
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division:

```cc
int num_batch_elements = 1;
for (int i = 0; i < num_batch_dimensions; ++i) {
  num_batch_elements *= data.shape().dim_size(i);
}
int num_value_elements = data.shape().num_elements() / num_batch_elements;
```

Since `data` is given by the `values` argument, `num_batch_elements` is 0.

### Patches
We have patched the issue in GitHub commit [da5ff2daf618591f64b2b62d9d9803951b945e9f](https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f).

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected.

### 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 Yakun Zhang and Ying Wang of Baidu X-Team.
Vulnerability ID: GHSA-qg48-85hg-mqc5
CWE: CWE-369
Severity score: 2.5
Project: https://github.com/tensorflow/tensorflow
Published: 2021-05-21
File: tensorflow/core/kernels/count_ops.cc
Commit message: Fix FPE issue with `tf.raw_ops.DenseCountSparseOutput`.

PiperOrigin-RevId: 370946862
Change-Id: I3752584ad04aaecb327ff6793a9640ac56acfe7a
Commit author: Amit Patankar

Vulnerable version (before fix):
 class DenseCount : public OpKernel {      int num_batch_elements = 1;     for (int i = 0; i < num_batch_dimensions; ++i) {       num_batch_elements *= data.shape().dim_size(i);     }     int num_value_elements = data.shape().num_elements() / num_batch_elements;

Fixed version (after fix):
 class DenseCount : public OpKernel {      int num_batch_elements = 1;     for (int i = 0; i < num_batch_dimensions; ++i) {       OP_REQUIRES(context, data.shape().dim_size(i) != 0,                   errors::InvalidArgument(                       "Invalid input: Shapes dimension cannot be 0."));       num_batch_elements *= data.shape().dim_size(i);     }     int num_value_elements = data.shape().num_elements() / num_batch_elements;

Patch:
patch: @@ -122,6 +122,9 @@ class DenseCount : public OpKernel {
 
     int num_batch_elements = 1;
     for (int i = 0; i < num_batch_dimensions; ++i) {
+      OP_REQUIRES(context, data.shape().dim_size(i) != 0,
+                  errors::InvalidArgument(
+                      "Invalid input: Shapes dimension cannot be 0."));
       num_batch_elements *= data.shape().dim_size(i);
     }
     int num_value_elements = data.shape().num_elements() / num_batch_elements;
path: tensorflow/core/kernels/count_ops.cc