(self, inputs)
Initialize buffers for recurrent internal units and outputs.
        Buffers are initialized as 2D tensors with second dimension being steps * batch_size.
        The second dimension is ordered as [s1b1, s1b2, ..., s1bn, s2b1, s2b2, ..., s2bn, ...]
        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.
        The second dimension is ordered as [s1b1, s1b2, ..., s1bn, s2b1, s2b2, ..., s2bn, ...]
        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 . The second dimension is ordered as [ s1b1 s1b2 ... s1bn s2b1 s2b2 ... s2bn ... ] 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.
        The second dimension is ordered as [s1b1, s1b2, ..., s1bn, s2b1, s2b2, ..., s2bn, ...]
        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:
            if self.x is not None:
                for buf in self.bufs_to_reset:
                    buf[:] = 0
            self.x = inputs.reshape(self.nin, self.nsteps * self.be.bsz)
            self.xs = get_steps(inputs, self.i_shape)
def init_buffers ( self , inputs ) : if self . x is None or self . x is not inputs : if self . x is not None : for buf in self . bufs_to_reset : buf [ : ] = 0 self . x = inputs . reshape ( self . nin , self . nsteps * self . be . bsz ) self . xs = get_steps ( inputs , self . i_shape )