(self, x, sequence_lengths)
return outputs, states
This call should not be used in DNC; it processes all inputs for all time steps!
        It's not a matter of the implementation of this function, it's just that DNC never processes the inputs for 
        all time steps in its controller.
        
        
        :return: outputs for all time steps
This call should not be used in DNC; it processes all inputs for all time steps!
        It's not a matter of the implementation of this function, it's just that DNC never processes the inputs for 
        all time steps in its controller.
        
        
        :return: outputs for all time steps
This call should not be used in DNC ; it processes all inputs for all time steps! It s not a matter of the implementation of this function it s just that DNC never processes the inputs for all time steps in its controller . : return : outputs for all time steps
def __call__(self, x, sequence_lengths):
        """
        This call should not be used in DNC; it processes all inputs for all time steps!
        It's not a matter of the implementation of this function, it's just that DNC never processes the inputs for 
        all time steps in its controller.
        
        
        :return: outputs for all time steps
        """
        # input should be of shape [batch_size, max_time, ...]
        outputs, states = tf.nn.dynamic_rnn(self.lstm_cell,
                                            x,
                                            dtype=tf.float32,
                                            sequence_length=sequence_lengths,
                                            swap_memory=True)
        # output is of shape [batch_size, max_time, output_size]
        if self.out_layer_exists:
            # TODO optimize this einsum
            outputs = tf.einsum("btm,mo->bto", outputs, self.weights) + self.biases

        # TODO this returns just the final state and not all of them? (needed for visualization purposes)
        return outputs, states
def __call__ ( self , x , sequence_lengths ) : # input should be of shape [batch_size, max_time, ...] outputs , states = tf . nn . dynamic_rnn ( self . lstm_cell , x , dtype = tf . float32 , sequence_length = sequence_lengths , swap_memory = True ) # output is of shape [batch_size, max_time, output_size] if self . out_layer_exists : # TODO optimize this einsum outputs = tf . einsum ( "btm,mo->bto" , outputs , self . weights ) + self . biases # TODO this returns just the final state and not all of them? (needed for visualization purposes) return outputs , states