code: def _get_model_file(
      pretrained_model_name_or_path: Union[str, Path],
      *,
      weights_name: str,
      subfolder: Optional[str],
      cache_dir: Optional[str],
      force_download: bool,
      proxies: Optional[Dict],
      resume_download: bool,
      local_files_only: bool,
      token: Optional[str],
      user_agent: Union[Dict, str, None],
      revision: Optional[str],
      commit_hash: Optional[str] = None,
  ):
      pretrained_model_name_or_path = str(pretrained_model_name_or_path)
      if os.path.isfile(pretrained_model_name_or_path):
          return pretrained_model_name_or_path
      elif os.path.isdir(pretrained_model_name_or_path):
          if os.path.isfile(
              os.path.join(pretrained_model_name_or_path, weights_name)
          ):
              # Load from a PyTorch checkpoint
              model_file = os.path.join(
                  pretrained_model_name_or_path, weights_name
              )
              return model_file
          elif subfolder is not None and os.path.isfile(
              os.path.join(
                  pretrained_model_name_or_path, subfolder, weights_name
              )
          ):
              model_file = os.path.join(
                  pretrained_model_name_or_path, subfolder, weights_name
              )
              return model_file
          else:
              raise EnvironmentError(
                  f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."
              )
      else:
          # 1. First check if deprecated way of loading from branches is used
          if (
              revision in DEPRECATED_REVISION_ARGS
              and (
                  weights_name == WEIGHTS_NAME
                  or weights_name == SAFETENSORS_WEIGHTS_NAME
              )
              and version.parse(version.parse(__version__).base_version)
              >= version.parse("0.22.0")
          ):
              try:
                  model_file = hf_hub_download(
                      pretrained_model_name_or_path,
                      filename=_add_variant(weights_name, revision),
                      cache_dir=cache_dir,
                      force_download=force_download,
                      proxies=proxies,
                      resume_download=resume_download,
                      local_files_only=local_files_only,
                      token=token,
                      user_agent=user_agent,
                      subfolder=subfolder,
                      revision=revision or commit_hash,
                  )
                  warnings.warn(
                      f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead.",
                      FutureWarning,
                  )
                  return model_file
              except:  # noqa: E722
                  warnings.warn(
                      f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(weights_name, revision)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(weights_name, revision)}' so that the correct variant file can be added.",
                      FutureWarning,
                  )
          try:
              # 2. Load model file as usual
              model_file = hf_hub_download(
                  pretrained_model_name_or_path,
                  filename=weights_name,
                  cache_dir=cache_dir,
                  force_download=force_download,
                  proxies=proxies,
                  resume_download=resume_download,
                  local_files_only=local_files_only,
                  token=token,
                  user_agent=user_agent,
                  subfolder=subfolder,
                  revision=revision or commit_hash,
              )
              return model_file
  
          except RepositoryNotFoundError:
              raise EnvironmentError(
                  f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
                  "listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
                  "token having permission to this repo with `token` or log in with `huggingface-cli "
                  "login`."
              )
          except RevisionNotFoundError:
              raise EnvironmentError(
                  f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
                  "this model name. Check the model page at "
                  f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."
              )
          except EntryNotFoundError:
              raise EnvironmentError(
                  f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."
              )
          except HTTPError as err:
              raise EnvironmentError(
                  f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"
              )
          except ValueError:
              raise EnvironmentError(
                  f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
                  f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
                  f" directory containing a file named {weights_name} or"
                  " \nCheckout your internet connection or see how to run the library in"
                  " offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."
              )
          except EnvironmentError:
              raise EnvironmentError(
                  f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
                  "'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
                  f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
                  f"containing a file named {weights_name}"
              )
  
imports: import os
  import re
  import sys
  import tempfile
  import traceback
  import warnings
  from pathlib import Path
  from typing import Dict, Optional, Union
  from uuid import uuid4
  from huggingface_hub import ModelCard, ModelCardData, create_repo, get_full_repo_name, hf_hub_download, upload_folder
  from huggingface_hub.constants import HF_HUB_CACHE, HF_HUB_DISABLE_TELEMETRY, HF_HUB_OFFLINE
  from huggingface_hub.file_download import REGEX_COMMIT_HASH
  from huggingface_hub.utils import EntryNotFoundError, RepositoryNotFoundError, RevisionNotFoundError, is_jinja_available, validate_hf_hub_args
  from packaging import version
  from requests import HTTPError
  from ..None import __version__
  from .constants import DEPRECATED_REVISION_ARGS, HUGGINGFACE_CO_RESOLVE_ENDPOINT, SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME
  from .import_utils import ENV_VARS_TRUE_VALUES, _flax_version, _jax_version, _onnxruntime_version, _torch_version, is_flax_available, is_onnx_available, is_torch_available
  from .logging import get_logger
label_desc: Use image node_id 6 for calling a global function with example usage: _get_model_file(pretrained_model_name_or_path) and returns: pretrained_model_name_or_path, model_file, model_file, model_file, model_file