code: def _gen_attention_mask(self, input: torch.Tensor) -> torch.Tensor:
      utterance_length = input.size(0)
      num_segs = math.ceil(utterance_length / self.segment_length)
  
      rc_mask = []
      query_mask = []
      summary_mask = []
  
      if self.use_mem:
          num_cols = 9
          # memory, right context, query segment
          rc_q_cols_mask = [idx in [1, 4, 7] for idx in range(num_cols)]
          # right context, query segment
          s_cols_mask = [idx in [4, 7] for idx in range(num_cols)]
          masks_to_concat = [rc_mask, query_mask, summary_mask]
      else:
          num_cols = 6
          # right context, query segment
          rc_q_cols_mask = [idx in [1, 4] for idx in range(num_cols)]
          s_cols_mask = None
          masks_to_concat = [rc_mask, query_mask]
  
      for seg_idx in range(num_segs):
          col_widths = self._gen_attention_mask_col_widths(
              seg_idx, utterance_length
          )
  
          rc_mask_block = _gen_attention_mask_block(
              col_widths,
              rc_q_cols_mask,
              self.right_context_length,
              input.device,
          )
          rc_mask.append(rc_mask_block)
  
          query_mask_block = _gen_attention_mask_block(
              col_widths,
              rc_q_cols_mask,
              min(
                  self.segment_length,
                  utterance_length - seg_idx * self.segment_length,
              ),
              input.device,
          )
          query_mask.append(query_mask_block)
  
          if s_cols_mask is not None:
              summary_mask_block = _gen_attention_mask_block(
                  col_widths, s_cols_mask, 1, input.device
              )
              summary_mask.append(summary_mask_block)
  
      attention_mask = (
          1 - torch.cat([torch.cat(mask) for mask in masks_to_concat])
      ).to(torch.bool)
      return attention_mask
  
imports: import math
  from typing import List, Optional, Tuple
  import torch
label_desc: Use image node_id 4 for calling the _EmformerImpl obj's underlying member method code with example usage: obj._gen_attention_mask(input) and returns: attention_mask