code: def gen_mem_attn_mask(
      mem,
      mem_valid_length,
      data,
      data_valid_length=None,
      dtype=np.float32,
      layout: str = "NT",
  ):
      """Generate the mask used for the decoder. All query slots are attended to the memory slots.
  
      In our implementation, 1 --> not masked, 0 --> masked
  
      Let's consider the data + mem with a batch of two samples:
  
      .. code-block:: none
  
          mem = [['I',   'can', 'now',   'use'],
                 ['May', 'the', 'force', '<PAD>']]
          mem_valid_length =
              [4, 3]
          data =
              [['numpy', 'in',    'Gluon@@', 'NLP'  ],
               ['be',    'with',  'you',     '<PAD>']]
          data_valid_length =
              [4, 3]
  
      For our example, the mask of the first sample is
  
      .. code-block:: none
  
                     ['I', 'can', 'now', 'use']
          'numpy':     1,    1,     1,     1
          'in':        1,    1,     1,     1
          'Gluon@@':   1,    1,     1,     1
          'NLP':       1,    1,     1,     1
  
      The mask of the second sample is
  
      .. code-block:: none
  
                     ['be', 'with', 'you', '<PAD>']
          'May':        1,    1,     1,     0
          'the':        1,    1,     1,     0
          'force':      1,    1,     1,     0
          '<PAD>':      0,    0,     0,     0
  
  
      Parameters
      ----------
      mem
         - layout = 'NT'
              Shape (batch_size, mem_length, C_mem)
         - layout = 'TN'
              Shape (mem_length, batch_size, C_mem)
  
      mem_valid_length :
          Shape (batch_size,)
      data
          - layout = 'NT'
              Shape (batch_size, query_length, C_data)
          - layout = 'TN'
              Shape (query_length, batch_size, C_data)
  
      data_valid_length :
          Shape (batch_size,)
      dtype
          Data type of the mask
      layout
          Layout of the data + mem tensor
  
      Returns
      -------
      mask :
          Shape (batch_size, query_length, mem_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)
          )
      mem_valid_length = mem_valid_length.astype(dtype)
      mem_steps = npx.arange_like(mem, axis=time_axis)  # (mem_length,)
      data_steps = npx.arange_like(
          data, axis=time_axis
      )  # (query_length,)
      mem_mask = (
          npx.reshape(mem_steps, (1, 1, -1))
          < npx.reshape(mem_valid_length, (-2, 1, 1))
      ).astype(
          dtype
      )  # (B, 1, mem_length)
      if data_valid_length is not None:
          data_valid_length = data_valid_length.astype(dtype)
          data_mask = (
              npx.reshape(data_steps, (1, -1, 1))
              < npx.reshape(data_valid_length, (-2, 1, 1))
          ).astype(
              dtype
          )  # (B, query_length, 1)
          mask = mem_mask * data_mask
      else:
          query_length_ones = np.ones_like(data_steps)
          mask = query_length_ones.reshape((1, -1, 1)) * mem_mask
      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 2 for calling a global function with example usage: gen_mem_attn_mask(mem, mem_valid_length, data, data_valid_length, dtype, layout) and returns: mask