return tf.raw_ops.Relu(features=res)
  elif act == 'RELU6':
    return tf.raw_ops.Relu6(features=res)
  elif act == 'TANH':
    return tf.raw_ops.Tanh(x=res)
  else:
    return res


@tf.RegisterGradient('NewConv2D')
def _conv_add_relu_grad(op: ops.Operation, grad):
  act = op.get_attr('act')
  y = op.outputs[0]
  if act == 'RELU':
    grad = gen_nn_ops.relu_grad(grad, y)
  elif act == 'RELU6':

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
    Variadic<TF_Tensor>:$input
  );