example, suppose y = f(x) and we wish to apply a custom function g for backprop
such that dx = g(dy). In Python,

```python
with tf.get_default_graph().gradient_override_map(
    {'IdentityN': 'OverrideGradientWithG'}):
  y, _ = identity_n([f(x), x])

@tf.RegisterGradient('OverrideGradientWithG')
def ApplyG(op, dy, _):
  return [None, g(dy)]  # Do not backprop to f(x).
```
  }];

  let arguments = (ins

        *   `tf.compat.v1.nn.fused_batch_norm` backprop to `offset` when
            `is_training=False`
        *   `tf.image.adjust_contrast` forward
        *   `tf.nn.depthwise_conv2d` backprop to `filter` when not using cuDNN
            convolution
        *   `tf.image.resize` with `method=ResizeMethod.NEAREST` backprop
        *   `tf.math.bincount` - TODO: confirm exception added