def __call__(self, x, w_init=None, b_init=None, fix_parameters=False):
        """
        Updates h and c by calling lstm function.

        Args:
            x (~nnabla.Variable): Input N-D array with shape (batch_size, input_size).
            w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.  
            b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`.
            fix_parameters (bool): When set to `True`, the weights and biases will not be updated.

        """
        self.h, self.c = lstm_cell(
            x, self.h, self.c, self.state_size,
            w_init, b_init,
            fix_parameters=fix_parameters, name=self.name)
        return self.h
def __call__ ( self , x , w_init = None , b_init = None , fix_parameters = False ) : self . h , self . c = lstm_cell ( x , self . h , self . c , self . state_size , w_init , b_init , fix_parameters = fix_parameters , name = self . name ) return self . h
Updates h and c by calling lstm function.

        Args:
            x (~nnabla.Variable): Input N-D array with shape (batch_size, input_size).
            w_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for weight. By default, it is initialized with :obj:`nnabla.initializer.UniformInitializer` within the range determined by :obj:`nnabla.initializer.calc_uniform_lim_glorot`.  
            b_init (:obj:`nnabla.initializer.BaseInitializer` or :obj:`numpy.ndarray`, optional): Initializer for bias. By default, it is initialized with zeros if `with_bias` is `True`.
            fix_parameters (bool): When set to `True`, the weights and biases will not be updated.