(self, inputs)
Computes this layer's output as part of a forward pass through the model.

    A layer subclass overrides this method to define how the layer computes
    outputs from inputs. If the layer depends on weights, state, or randomness
    as part of the computation, the needed information can be accessed as
    properties of the layer object: `self.weights`, `self.state`, and
    `self.rng`. (See numerous examples in `trax.layers.core`.)

    Args:
      inputs: Zero or more input tensors, packaged as described in the `Layer`
          class docstring.

    Returns:
      Zero or more output tensors, packaged as described in the `Layer` class
      docstring.
Computes this layer's output as part of a forward pass through the model.
Computes this layer s output as part of a forward pass through the model .
def forward(self, inputs):
    """Computes this layer's output as part of a forward pass through the model.

    A layer subclass overrides this method to define how the layer computes
    outputs from inputs. If the layer depends on weights, state, or randomness
    as part of the computation, the needed information can be accessed as
    properties of the layer object: `self.weights`, `self.state`, and
    `self.rng`. (See numerous examples in `trax.layers.core`.)

    Args:
      inputs: Zero or more input tensors, packaged as described in the `Layer`
          class docstring.

    Returns:
      Zero or more output tensors, packaged as described in the `Layer` class
      docstring.
    """
    raise NotImplementedError
def forward ( self , inputs ) : raise NotImplementedError