def forward_memory(self, input, h, m):
        """First part of forward pass to construct the memory state update."""

        input_to_memory = input if self.architecture['ux'] else input.new_empty((0,))
        xh = torch.cat((input_to_memory, h), dim=-1)

        # Construct the update features
        u = self.W_uxh(xh)  # (batch, memory_size)
        return u
First part of forward pass to construct the memory state update.
analyze/convnexts4nd/src/models/sequence/rnns/cells/memory.py