(inputs, seg_idx)
return seg_mask
get padding mask from the input token idx
  inputs: [batch_size, time_steps]
  mask: [batch_size, time_steps]
get padding mask from the input token idx
  inputs: [batch_size, time_steps]
  mask: [batch_size, time_steps]
get padding mask from the input token idx inputs : [ batch_size time_steps ] mask : [ batch_size time_steps ]
def get_seg_mask_from_token_idx(inputs, seg_idx):
  """
  get padding mask from the input token idx
  inputs: [batch_size, time_steps]
  mask: [batch_size, time_steps]
  """
  seg_mask = tf.cast(tf.math.equal(inputs, seg_idx), tf.int32)
  return seg_mask
def get_seg_mask_from_token_idx ( inputs , seg_idx ) : seg_mask = tf . cast ( tf . math . equal ( inputs , seg_idx ) , tf . int32 ) return seg_mask