(self, inputs)
Initialize buffers for recurrent internal units and outputs.
        Buffers are initialized as 2D tensors with second dimension being steps * batch_size
        A list of views are created on the buffer for easy manipulation of data
        related to a certain time step

        Arguments:
            inputs (Tensor): input data as 2D tensor. The dimension is
                             (input_size, sequence_length * batch_size)
Initialize buffers for recurrent internal units and outputs.
        Buffers are initialized as 2D tensors with second dimension being steps * batch_size
        A list of views are created on the buffer for easy manipulation of data
        related to a certain time step
Initialize buffers for recurrent internal units and outputs . Buffers are initialized as 2D tensors with second dimension being steps * batch_size A list of views are created on the buffer for easy manipulation of data related to a certain time step
def init_buffers(self, inputs):
        """
        Initialize buffers for recurrent internal units and outputs.
        Buffers are initialized as 2D tensors with second dimension being steps * batch_size
        A list of views are created on the buffer for easy manipulation of data
        related to a certain time step

        Arguments:
            inputs (Tensor): input data as 2D tensor. The dimension is
                             (input_size, sequence_length * batch_size)
        """
        if self.x is None or self.x is not inputs:
            self.x = inputs
def init_buffers ( self , inputs ) : if self . x is None or self . x is not inputs : self . x = inputs