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    specific existing ones.  For example, you can clone a stack of layers, while

    connecting the topmost ones to different inputs.

    For example, consider a stack of dense layers that depend on an input:

    >>> input = Feature(shape=(None, 100))

    >>> dense1 = Dense(100, in_layers=input)

    >>> dense2 = Dense(100, in_layers=dense1)

    >>> dense3 = Dense(100, in_layers=dense2)

    The following will clone all three dense layers, but not the input layer.

    Instead, the input to the first dense layer will be a different layer

    specified in the replacements map.

    >>> replacements = {input: new_input}

    >>> dense3_copy = dense3.copy(replacements)

    Parameters

    ----------

    replacements: map

  This layer copies its input directly to its output, but reports that all

  gradients of its output are zero.  This means, for example, that optimizers

  will not try to optimize anything "upstream" of this layer."""

  will not try to optimize anything "upstream" of this layer.

  For example, suppose you have pre-trained a stack of layers to perform a

  calculation.  You want to use the result of that calculation as the input to

  another layer, but because they are already pre-trained, you do not want the

  optimizer to modify them.  You can wrap the output in a StopGradient layer,

  then use that as the input to the next layer."""

  def __init__(self, in_layers=None, **kwargs):

    super(StopGradient, self).__init__(in_layers, **kwargs)