code: def gen_self_attn_mask(
      data,
      valid_length=None,
      dtype: type = np.float32,
      attn_type: str = "full",
      layout: str = "NT",
  ):
      """Generate the mask used for the encoder, i.e, self-attention.
  
      In our implementation, 1 --> not masked, 0 --> masked
  
      Let's consider the data with two samples:
  
      .. code-block:: none
  
          data =
              [['I',   'can', 'now',   'use', 'numpy', 'in',  'Gluon@@', 'NLP'  ],
               ['May', 'the', 'force', 'be',  'with',  'you', '<PAD>',   '<PAD>']]
          valid_length =
              [8, 6]
  
      - attn_type = 'causal'
          Each token will attend to itself + the tokens before.
          It will not attend to tokens in the future.
  
          For our example, the mask of the first sample is
  
          .. code-block:: none
  
                         ['I', 'can', 'now', 'use', 'numpy', 'in', 'Gluon@@', 'NLP']
              'I':         1,    0,     0,     0,      0,     0,      0,      0
              'can':       1,    1,     0,     0,      0,     0,      0,      0
              'now':       1,    1,     1,     0,      0,     0,      0,      0
              'use':       1,    1,     1,     1,      0,     0,      0,      0
              'numpy':     1,    1,     1,     1,      1,     0,      0,      0
              'in':        1,    1,     1,     1,      1,     1,      0,      0
              'Gluon@@':   1,    1,     1,     1,      1,     1,      1,      0
              'NLP':       1,    1,     1,     1,      1,     1,      1,      1
  
          The mask of the second sample is
  
          .. code-block:: none
  
                         ['May', 'the', 'force', 'be', 'with', 'you', '<PAD>', '<PAD>']
              'May':        1,    0,     0,     0,      0,     0,      0,      0
              'the':        1,    1,     0,     0,      0,     0,      0,      0
              'force':      1,    1,     1,     0,      0,     0,      0,      0
              'be':         1,    1,     1,     1,      0,     0,      0,      0
              'with':       1,    1,     1,     1,      1,     0,      0,      0
              'you':        1,    1,     1,     1,      1,     1,      0,      0
              '<PAD>':      0,    0,     0,     0,      0,     0,      0,      0
              '<PAD>':      0,    0,     0,     0,      0,     0,      0,      0
  
  
      - attn_type = 'full'
          Each token will attend to both the tokens before and in the future
  
          For our example, the mask of the first sample is
  
          .. code-block:: none
  
                         ['I', 'can', 'now', 'use', 'numpy', 'in', 'Gluon@@', 'NLP']
              'I':         1,    1,     1,     1,      1,     1,      1,      1
              'can':       1,    1,     1,     1,      1,     1,      1,      1
              'now':       1,    1,     1,     1,      1,     1,      1,      1
              'use':       1,    1,     1,     1,      1,     1,      1,      1
              'numpy':     1,    1,     1,     1,      1,     1,      1,      1
              'in':        1,    1,     1,     1,      1,     1,      1,      1
              'Gluon@@':   1,    1,     1,     1,      1,     1,      1,      1
              'NLP':       1,    1,     1,     1,      1,     1,      1,      1
  
          The mask of the second sample is
  
          .. code-block:: none
  
                         ['May', 'the', 'force', 'be', 'with', 'you', '<PAD>', '<PAD>']
              'May':        1,    1,     1,     1,      1,     1,      0,      0
              'the':        1,    1,     1,     1,      1,     1,      0,      0
              'force':      1,    1,     1,     1,      1,     1,      0,      0
              'be':         1,    1,     1,     1,      1,     1,      0,      0
              'with':       1,    1,     1,     1,      1,     1,      0,      0
              'you':        1,    1,     1,     1,      1,     1,      0,      0
              '<PAD>':      0,    0,     0,     0,      0,     0,      0,      0
              '<PAD>':      0,    0,     0,     0,      0,     0,      0,      0
  
      Parameters
      ----------
      data
          The data.
  
          - layout = 'NT'
              Shape (batch_size, seq_length, C)
          - layout = 'TN'
              Shape (seq_length, batch_size, C)
  
      valid_length
          Shape (batch_size,)
      dtype
          Data type of the mask
      attn_type
          Can be 'full' or 'causal'
      layout
          The layout of the data
  
      Returns
      -------
      mask
          Shape (batch_size, seq_length, seq_length)
      """
      if layout == "NT":
          batch_axis, time_axis = 0, 1
      elif layout == "TN":
          batch_axis, time_axis = 1, 0
      else:
          raise NotImplementedError(
              "Unsupported layout={}".format(layout)
          )
      if attn_type == "full":
          if valid_length is not None:
              valid_length = valid_length.astype(dtype)
              steps = npx.arange_like(
                  data, axis=time_axis
              )  # (seq_length,)
              mask1 = npx.reshape(steps, (1, 1, -1)) < npx.reshape(
                  valid_length, (-2, 1, 1)
              )
              mask2 = npx.reshape(steps, (1, -1, 1)) < npx.reshape(
                  valid_length, (-2, 1, 1)
              )
              mask = mask1 * mask2
          else:
              # TODO(sxjscience) optimize
              seq_len_ones = np.ones_like(
                  npx.arange_like(data, axis=time_axis)
              )  # (seq_length,)
              batch_ones = np.ones_like(
                  npx.arange_like(data, axis=batch_axis)
              )  # (batch_size,)
              mask = (
                  batch_ones.reshape((-1, 1, 1))
                  * seq_len_ones.reshape((1, -1, 1))
                  * seq_len_ones.reshape((1, 1, -1))
              )
      elif attn_type == "causal":
          steps = npx.arange_like(data, axis=time_axis)
          # mask: (seq_length, seq_length)
          # batch_mask: (batch_size, seq_length)
          mask = (
              np.expand_dims(steps, axis=0)
              <= np.expand_dims(steps, axis=1)
          ).astype(dtype)
          if valid_length is not None:
              valid_length = valid_length.astype(dtype)
              batch_mask = (
                  np.expand_dims(steps, axis=0)
                  < np.expand_dims(valid_length, axis=-1)
              ).astype(dtype)
              mask = mask * np.expand_dims(batch_mask, axis=-1)
          else:
              batch_ones = np.ones_like(
                  npx.arange_like(data, axis=batch_axis), dtype=dtype
              )  # (batch_size,)
              mask = mask * batch_ones.reshape((-1, 1, 1))
      else:
          raise NotImplementedError
      return mask.astype(np.bool)
  
imports: import math
  import mxnet
  from mxnet import np, npx
  import numpy
  from mxnet.gluon.block import HybridBlock
  from mxnet.gluon import nn
  from .op import l2_normalize
  from .layers import SinusoidalPositionalEmbedding, BucketPositionalEmbedding, LearnedPositionalEmbedding
  from typing import Optional
label_desc: Use image node_id 1 for calling a global function with example usage: gen_self_attn_mask(data, valid_length, dtype, attn_type, layout) and returns: mask