original_docstring: Pads the input tensor with 0s along the first dimension up to the length.
          Args:
          t: the input tensor, assuming the rank is at least 1.
          length: a tensor of shape [1]  or an integer, indicating the first dimension
            of the input tensor t after padding, assuming length <= t.shape[0].
          Returns:
          padded_t: the padded tensor, whose first dimension is length. If the length
            is an integer, the first dimension of padded_t is set to length
            statically.
          
docstring: Pads the input tensor with 0s along the first dimension up to the length.
  Args:
  t: the input tensor, assuming the rank is at least 1.
  length: a tensor of shape [1]  or an integer, indicating the first dimension
  of the input tensor t after padding, assuming length <= t.shape[0].
  Returns:
  padded_t: the padded tensor, whose first dimension is length. If the length
  is an integer, the first dimension of padded_t is set to length
  statically.
docstring_tokens: Pads the input tensor with 0s along the first dimension up to the length . Args : t : the input tensor assuming the rank is at least 1 . length : a tensor of shape [ 1 ] or an integer indicating the first dimension of the input tensor t after padding assuming length < = t . shape [ 0 ] . Returns : padded_t : the padded tensor whose first dimension is length . If the length is an integer the first dimension of padded_t is set to length statically .
code: def _pad_tensor(self, t, length, value=0):
          """Pads the input tensor with 0s along the first dimension up to the length.
          Args:
          t: the input tensor, assuming the rank is at least 1.
          length: a tensor of shape [1]  or an integer, indicating the first dimension
            of the input tensor t after padding, assuming length <= t.shape[0].
          Returns:
          padded_t: the padded tensor, whose first dimension is length. If the length
            is an integer, the first dimension of padded_t is set to length
            statically.
          """
          t_rank = tf.rank(t)
          t_shape = tf.shape(t)
          t_d0 = t_shape[0]
          pad_d0 = tf.expand_dims(length - t_d0, 0)
          pad_shape = tf.cond(
            tf.greater(t_rank, 1), lambda: tf.concat([pad_d0, t_shape[1:]], 0),
            lambda: tf.expand_dims(length - t_d0, 0))
          padded_t = tf.concat([t, value+tf.zeros(pad_shape, dtype=t.dtype)], 0)
  
          t_shape = padded_t.get_shape().as_list()
          t_shape[0] = length
          padded_t.set_shape(t_shape)
  
          return padded_t
code_tokens: def _pad_tensor ( self , t , length , value = 0 ) : t_rank = tf . rank ( t ) t_shape = tf . shape ( t ) t_d0 = t_shape [ 0 ] pad_d0 = tf . expand_dims ( length - t_d0 , 0 ) pad_shape = tf . cond ( tf . greater ( t_rank , 1 ) , lambda : tf . concat ( [ pad_d0 , t_shape [ 1 : ] ] , 0 ) , lambda : tf . expand_dims ( length - t_d0 , 0 ) ) padded_t = tf . concat ( [ t , value + tf . zeros ( pad_shape , dtype = t . dtype ) ] , 0 ) t_shape = padded_t . get_shape ( ) . as_list ( ) t_shape [ 0 ] = length padded_t . set_shape ( t_shape ) return padded_t
short_docstring: Pads the input tensor with 0s along the first dimension up to the length.
short_docstring_tokens: Pads the input tensor with 0s along the first dimension up to the length .
comment: """Pads the input tensor with 0s along the first dimension up to the length.
          Args:
          t: the input tensor, assuming the rank is at least 1.
          length: a tensor of shape [1]  or an integer, indicating the first dimension
            of the input tensor t after padding, assuming length <= t.shape[0].
          Returns:
          padded_t: the padded tensor, whose first dimension is length. If the length
            is an integer, the first dimension of padded_t is set to length
            statically.
          """