code: def make_attention_mask_3d(source_block, target_block):
      """
      Returns a 3-dimensional (3-D) attention mask
      :param source_block: 1-D array
      :param target_block: 1-D array
      """
      mask = (target_block[:, None, :] >= 1) * (
          source_block[:, :, None] >= 1
      )
      # (batch, source_length, target_length)
      # mask = mask.astype(np.int64)
      return mask
  
file_path: megatron-lm/megatron/data/t5_dataset.py
imports: import collections
  import numpy
  import torch
  from megatron import get_tokenizer
  from megatron.data.dataset_utils import create_masked_lm_predictions, get_samples_mapping
label_desc: Use image node_id 4 for calling a global function with example usage: make_attention_mask_3d(source_block, target_block) and returns: mask