code: def import_model(
      name: Tag | str,
      model_name_or_path: str | os.PathLike[str],
      *,
      proxies: dict[str, str] | None = None,
      revision: str = "main",
      variant: str | None = None,
      pipeline_class: str
      | type[diffusers.DiffusionPipeline]
      | None = None,
      sync_with_hub_version: bool = False,
      signatures: dict[str, ModelSignatureDict | ModelSignature]
      | None = None,
      labels: dict[str, str] | None = None,
      custom_objects: dict[str, t.Any] | None = None,
      external_modules: t.List[ModuleType] | None = None,
      metadata: dict[str, t.Any] | None = None,
      # ...
  ) -> bentoml.Model:
      """
      Import Diffusion model from a artifact URI to the BentoML model store.
  
      Args:
          name:
              The name to give to the model in the BentoML store. This must be a valid
              :obj:`~bentoml.Tag` name.
          model_name_or_path:
              Can be either:
              - A string, the *repo id* of a pretrained pipeline hosted inside a model repo on
                https://huggingface.co/ Valid repo ids have to be located under a user or organization name, like
                `CompVis/ldm-text2im-large-256`.
              - A path to a *directory* containing pipeline weights saved using
                [`~DiffusionPipeline.save_pretrained`], e.g., `./my_pipeline_directory/`.
          proxies (`Dict[str, str]`, *optional*):
              A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
              'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
          revision (`str`, *optional*, defaults to `"main"`):
              The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
              git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
              identifier allowed by git.
          variant (`str`, *optional*):
              Variant of the model to import. For example there's "fp16" and "fp32" variant for "DeepFloyd/IF-I-XL-v1.0".
              This may save download bandwidth and local disk space.
          sync_with_hub_version (`bool`, default to False):
              If sync_with_hub_version is true, then the model imported by
          signatures:
              Signatures of predict methods to be used. If not provided, the signatures
              default to {"__call__": {"batchable": False}}. See
              :obj:`~bentoml.types.ModelSignature` for more details.
          labels:
              A default set of management labels to be associated with the model. For
              example: ``{"training-set": "data-v1"}``.
          custom_objects:
              Custom objects to be saved with the model. An example is
              ``{"my-normalizer": normalizer}``. Custom objects are serialized with
              cloudpickle.
          metadata:
              Metadata to be associated with the model. An example is ``{"param_a": .2}``.
  
              Metadata is intended for display in a model management UI and therefore all
              values in metadata dictionary must be a primitive Python type, such as
              ``str`` or ``int``.
  
      Returns:
          A :obj:`~bentoml.Model` instance referencing a saved model in the local BentoML
          model store.
  
      Example:
  
      .. code-block:: python
  
          import bentoml
  
          bentoml.diffusers.import_model(
              'my_sd15_model',
              "runwayml/stable-diffusion-v1-5",
              signatures={
                  "__call__": {"batchable": False},
              }
          )
      """
  
      tag = Tag.from_taglike(name)
  
      try:
          model = bentoml.models.get(tag)
          return model
      except bentoml.exceptions.NotFound:
          pass
  
      if sync_with_hub_version:
          if tag.version is not None:
              logger.warn(
                  f"sync_with_hub_version is True, user provided version {tag.version} may be overridden by huggingface hub's commit hash"
              )
  
      context = ModelContext(
          framework_name="diffusers",
          framework_versions={"diffusers": diffusers.__version__},
      )
  
      if signatures is None:
          signatures = {
              "__call__": {"batchable": False},
          }
          logger.info(
              'Using the default model signature for diffusers (%s) for model "%s".',
              signatures,
              name,
          )
  
      if pipeline_class and isinstance(pipeline_class, str):
          pipeline_class = t.cast(
              type[diffusers.DiffusionPipeline],
              _str2cls(pipeline_class),
          )
  
      options_dict: dict[str, str] = {}
      if pipeline_class:
          cls_str = (
              f"{pipeline_class.__module__}.{pipeline_class.__name__}"
          )
          options_dict["pipeline_class"] = cls_str
      if variant:
          options_dict["variant"] = variant
  
      options = (
          DiffusersOptions(**options_dict) if options_dict else None
      )
  
      if os.path.isdir(model_name_or_path):
          src_dir = model_name_or_path
          if sync_with_hub_version:
              raise BentoMLException(
                  "Cannot sync version with huggingface hub when importing a local model"
              )
  
      elif pipeline_class:
          src_dir = pipeline_class.download(
              model_name_or_path,
              proxies=proxies,
              revision=revision,
              variant=variant,
          )
  
          if sync_with_hub_version:
              from huggingface_hub.file_download import (
                  REGEX_COMMIT_HASH,
              )
  
              version = extract_commit_hash(src_dir, REGEX_COMMIT_HASH)
              if version is not None:
                  if variant is not None:
                      version = version + "-" + variant
                  tag.version = version
  
      else:
          from huggingface_hub import snapshot_download
  
          src_dir = snapshot_download(
              model_name_or_path,
              proxies=proxies,
              revision=revision,
          )
  
          if sync_with_hub_version:
              from huggingface_hub.file_download import (
                  REGEX_COMMIT_HASH,
              )
  
              version = extract_commit_hash(src_dir, REGEX_COMMIT_HASH)
              if version is not None:
                  tag.version = version
  
      with bentoml.models.create(
          tag,
          module=MODULE_NAME,
          api_version=API_VERSION,
          signatures=signatures,
          labels=labels,
          options=options,
          custom_objects=custom_objects,
          external_modules=external_modules,
          metadata=metadata,
          context=context,
      ) as bento_model:
          diffusion_model_dir = bento_model.path_of(
              DIFFUSION_MODEL_FOLDER
          )
          ignore = shutil.ignore_patterns(".git")
  
          model_config_file = os.path.join(
              src_dir, DIFFUSION_MODEL_CONFIG_FILE
          )
          if not os.path.exists(model_config_file):
              raise BentoMLException(
                  f'artifact "{src_dir}" is not a Diffusion model'
              )
  
          shutil.copytree(
              src_dir,
              diffusion_model_dir,
              symlinks=False,
              ignore=ignore,
          )
  
          return bento_model
  
imports: from __future__ import annotations
  import logging
  import os
  import shutil
  import typing
  from typing import TYPE_CHECKING
  import attr
  import bentoml
  from bentoml import Tag
  from bentoml.exceptions import BentoMLException
  from bentoml.exceptions import MissingDependencyException
  from bentoml.exceptions import NotFound
  from bentoml.models import ModelContext
  from ..models.model import PartialKwargsModelOptions
  from .utils.transformers import extract_commit_hash
label_desc: Use image node_id 7 for calling a global function with example usage: import_model(name, model_name_or_path) and returns: model, bento_model