code: def _make_causal_mask_patched(
      input_ids_shape: torch.Size,
      dtype: torch.dtype,
      device: torch.device,
      past_key_values_length: int = 0,
      sliding_window: Optional[int] = None,
  ):
      """
      Make causal mask used for bi-directional self-attention.
      """
      # We add self in the signature because `self._make_causal_mask` is used elsewhere in the class definition, despite the method being a staticmethod.
      bsz, tgt_len = input_ids_shape
      mask = torch.full(
          (tgt_len, tgt_len), torch.finfo(dtype).min, device=device
      )
      mask_cond = torch.arange(mask.size(-1), device=device)
      mask.masked_fill_(
          mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0
      )
  
      mask = mask.to(dtype)
  
      if past_key_values_length > 0:
          mask = torch.cat(
              [
                  torch.zeros(
                      tgt_len,
                      past_key_values_length,
                      dtype=dtype,
                      device=device,
                  ),
                  mask,
              ],
              dim=-1,
          )
  
      # add lower triangular sliding window mask if necessary
      if sliding_window is not None:
          diagonal = past_key_values_length - sliding_window + 1
  
          # NOTE: adding dtype=torch.int64 here for triu to be supported by ORT: https://github.com/microsoft/onnxruntime/issues/16189
          context_mask = 1 - torch.triu(
              torch.ones_like(mask, dtype=torch.int64),
              diagonal=diagonal,
          )
          mask.masked_fill_(context_mask.bool(), torch.finfo(dtype).min)
  
      return mask[None, None, :, :].expand(
          bsz, 1, tgt_len, tgt_len + past_key_values_length
      )
  
imports: import dataclasses
  import functools
  import inspect
  import math
  import sys
  import types
  from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
  import transformers
  from packaging import version
  from transformers.models.speecht5.modeling_speecht5 import SpeechT5EncoderWithSpeechPrenet
  from transformers.utils import is_torch_available
  from ...configuration_utils import _transformers_version
  from ...utils import logging
label_desc: Use image node_id 4 for calling a global function with example usage: _make_causal_mask_patched(input_ids_shape, dtype, device, past_key_values_length, sliding_window) and returns: mask