(self, shared_outputs=None)
Allocate output buffer to store activations from fprop.
        Don't reallocate if it already exists.
        Only allocate space if layer owns its own output (e.g., bias and activation work
        in place, so do not own their output).
        Outputs can be allocated from a pre-allocated pool if shared_outputs is provided.

        Arguments:
            shared_outputs (Tensor, optional): pre-allocated tensor for activations to be
                                               computed into
Allocate output buffer to store activations from fprop.
        Don't reallocate if it already exists.
        Only allocate space if layer owns its own output (e.g., bias and activation work
        in place, so do not own their output).
        Outputs can be allocated from a pre-allocated pool if shared_outputs is provided.
Allocate output buffer to store activations from fprop . Don t reallocate if it already exists . Only allocate space if layer owns its own output ( e . g . bias and activation work in place so do not own their output ) . Outputs can be allocated from a pre - allocated pool if shared_outputs is provided .
def allocate(self, shared_outputs=None):
        """
        Allocate output buffer to store activations from fprop.
        Don't reallocate if it already exists.
        Only allocate space if layer owns its own output (e.g., bias and activation work
        in place, so do not own their output).
        Outputs can be allocated from a pre-allocated pool if shared_outputs is provided.

        Arguments:
            shared_outputs (Tensor, optional): pre-allocated tensor for activations to be
                                               computed into

        """
        if self.outputs:
            return
        if self.owns_output:
            self.outputs = self.be.iobuf(self.out_shape, shared=shared_outputs,
                                         parallelism=self.parallelism)
def allocate ( self , shared_outputs = None ) : if self . outputs : return if self . owns_output : self . outputs = self . be . iobuf ( self . out_shape , shared = shared_outputs , parallelism = self . parallelism )