tf.identity_n

Returns a list of tensors with the same shapes and contents as the input

tf.identity_n(
    input, name=None
)

tensors.

This op can be used to override the gradient for complicated functions. For example, suppose y = f(x) and we wish to apply a custom function g for backprop such that dx = g(dy). In 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).

Args:

• input: A list of Tensor objects.
• name: A name for the operation (optional).

Returns:

A list of Tensor objects. Has the same type as input.