code: def can_use_efficient_attention(
      params: SDPAParams, debug: bool = False
  ) -> bool:
      r"""Check if efficient_attention can be utilized in scaled_dot_product_attention.
  
      Args:
          params: An instance of SDPAParams containing the tensors for query,
                  key, value, an optional attention mask, dropout rate, and
                  a flag indicating if the attention is causal.
          debug: Whether to logging.warn with information as to why efficient_attention could not be run.
              Defaults to False.
  
      Returns:
          True if efficient_attention can be used with the given parameters; otherwise, False.
  
      Note:
          This function is dependent on a CUDA-enabled build of PyTorch. It will return False
          in non-CUDA environments.
      """
      return torch._C._can_use_mem_efficient_attention(params, debug)
  
imports: import contextlib
  from typing import Union
  import torch
  from torch._C import _SDPAParams, _SDPBackend
label_desc: Use image node_id 10 for calling a global function with example usage: can_use_efficient_attention(params, debug) and returns: torch