code: def _prepare_4d_causal_attention_mask_for_sdpa_patched(
      attention_mask: Optional[torch.Tensor],
      input_shape: Union[torch.Size, Tuple, List],
      inputs_embeds: torch.Tensor,
      past_key_values_length: int,
      sliding_window: Optional[int] = None,
  ):
      """
      Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`.
  
      In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and
      `key_value_length == query_length`, and rely instead on SDPA `is_causal` argument to use causal/non-causal masks,
      allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
      """
      attn_mask_converter = AttentionMaskConverter(
          is_causal=True, sliding_window=sliding_window
      )
  
      key_value_length = input_shape[-1] + past_key_values_length
  
      # 4d mask is passed through the layers
      if attention_mask is not None:
          attention_mask = attn_mask_converter.to_4d(
              attention_mask,
              input_shape[-1],
              key_value_length=key_value_length,
              dtype=inputs_embeds.dtype,
          )
      else:
          attention_mask = attn_mask_converter.to_causal_4d(
              input_shape[0],
              input_shape[-1],
              key_value_length,
              dtype=inputs_embeds.dtype,
              device=inputs_embeds.device,
          )
  
      # NOTE: For the ONNX export we remove the setting of attention_mask to None in some specific cases, and we do NOT call _unmask_unattended
      # that can not be exported to ONNX and is very specific to PyTorch memory-efficient attention backend anyway.
  
      return attention_mask
  
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 5 for calling a global function with example usage: _prepare_4d_causal_attention_mask_for_sdpa_patched(attention_mask, input_shape, inputs_embeds, past_key_values_length, sliding_window) and returns: attention_mask