code: def _load_triton_auto_tune_cache(
      f: BinaryIO, strict: bool = True, verbose: bool = False
  ) -> None:
      caches = pickle.load(f)
      if strict:
          loaded_func_name = set(caches.keys())
          tuneable_func_name = set(_tuneable_triton_kernels.keys())
          if loaded_func_name != tuneable_func_name:
              raise ValueError(
                  f"Tuneable Triton kernels don't match with provided auto-tuning cache file {f}\n"
                  f"Missing kernel caches: {tuneable_func_name - loaded_func_name}\n"
                  f"Unexpected kernel caches: {loaded_func_name - tuneable_func_name}"
              )
      for func_name, cache in caches.items():
          if func_name not in _tuneable_triton_kernels:
              raise ValueError(
                  f"{func_name} from {f} doesn't match any tuneable Triton kernels"
              )
          _tuneable_triton_kernels[func_name].cache = cache
      if verbose:
          print(f"Triton kernel auto-tuning caches loaded from {f}")
  
file_path: apex/apex/contrib/openfold_triton/__init__.py
imports: import pickle
  from collections import OrderedDict
  from copy import deepcopy
  from io import BytesIO
  from typing import BinaryIO, Union
  import torch
  from triton.runtime.autotuner import Autotuner, Heuristics
  from triton.runtime.jit import JITFunction
  from apex.contrib.openfold_triton._layer_norm_backward_kernels import _layer_norm_backward_dw_db_partial, _layer_norm_backward_dw_db_partial_strided, _layer_norm_backward_dx, _layer_norm_backward_dx_strided
  from apex.contrib.openfold_triton._layer_norm_forward_kernels import _layer_norm_forward, _layer_norm_forward_strided
  from apex.contrib.openfold_triton.layer_norm import LayerNormSmallShapeOptImpl
  from apex.contrib.openfold_triton.mha import AttnBiasJIT, AttnNoBiasJIT, AttnTri, CanSchTriMHA
label_desc: Use image node_id 3 for calling a global function with example usage: _load_triton_auto_tune_cache(f, strict, verbose) without return types