code: def waitk_p_choose(
      tgt_len: int,
      src_len: int,
      bsz: int,
      waitk_lagging: int,
      key_padding_mask: Optional[Tensor] = None,
      incremental_state: Optional[
          Dict[str, Dict[str, Optional[Tensor]]]
      ] = None,
  ):
      max_src_len = src_len
      if incremental_state is not None:
          # Retrieve target length from incremental states
          # For inference the length of query is always 1
          max_tgt_len = incremental_state["steps"]["tgt"]
          assert max_tgt_len is not None
          max_tgt_len = int(max_tgt_len)
      else:
          max_tgt_len = tgt_len
  
      if max_src_len < waitk_lagging:
          if incremental_state is not None:
              max_tgt_len = 1
          return torch.zeros(bsz, max_tgt_len, max_src_len)
  
      # Assuming the p_choose looks like this for wait k=3
      # src_len = 6, max_tgt_len = 5
      #   [0, 0, 1, 0, 0, 0, 0]
      #   [0, 0, 0, 1, 0, 0, 0]
      #   [0, 0, 0, 0, 1, 0, 0]
      #   [0, 0, 0, 0, 0, 1, 0]
      #   [0, 0, 0, 0, 0, 0, 1]
      # linearize the p_choose matrix:
      # [0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0...]
      # The indices of linearized matrix that equals 1 is
      # 2 + 6 * 0
      # 3 + 6 * 1
      # ...
      # n + src_len * n + k - 1 = n * (src_len + 1) + k - 1
      # n from 0 to max_tgt_len - 1
      #
      # First, generate the indices (activate_indices_offset: bsz, max_tgt_len)
      # Second, scatter a zeros tensor (bsz, max_tgt_len * src_len)
      # with activate_indices_offset
      # Third, resize the tensor to (bsz, max_tgt_len, src_len)
  
      activate_indices_offset = (
          (
              torch.arange(max_tgt_len) * (max_src_len + 1)
              + waitk_lagging
              - 1
          )
          .unsqueeze(0)
          .expand(bsz, max_tgt_len)
          .long()
      )
  
      if key_padding_mask is not None:
          if key_padding_mask[:, 0].any():
              # Left padding
              activate_indices_offset += key_padding_mask.sum(
                  dim=1, keepdim=True
              )
  
      # Need to clamp the indices that are too large
      activate_indices_offset = activate_indices_offset.clamp(
          0,
          min([max_tgt_len, max_src_len - waitk_lagging + 1])
          * max_src_len
          - 1,
      )
  
      p_choose = torch.zeros(bsz, max_tgt_len * max_src_len)
  
      p_choose = p_choose.scatter(1, activate_indices_offset, 1.0).view(
          bsz, max_tgt_len, max_src_len
      )
  
      if key_padding_mask is not None:
          p_choose = p_choose.to(key_padding_mask)
          p_choose = p_choose.masked_fill(
              key_padding_mask.unsqueeze(1), 0
          )
  
      if incremental_state is not None:
          p_choose = p_choose[:, -1:]
  
      return p_choose.float()
  
imports: from typing import Optional, Dict
  from torch import Tensor
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: waitk_p_choose(tgt_len, src_len, bsz, waitk_lagging, key_padding_mask, incremental_state) and returns: p_choose, torch