code: def pretrained_model(
      framework: t.Literal["pt", "tf", "jax"], model: str, /
  ) -> (
      transformers.PreTrainedModel
      | transformers.FlaxPreTrainedModel
      | transformers.TFPreTrainedModel
  ):
      if framework in MODEL_PRETRAINED_MAPPING[model]:
          return getattr(
              transformers, MODEL_PRETRAINED_MAPPING[model][framework]
          ).from_pretrained(model)
      raise ValueError(
          f"Framework {framework} doesn't have a pretrained class implementation."
      )
  
imports: from __future__ import annotations
  import importlib
  import typing
  import numpy
  import requests
  import tensorflow
  import transformers
  from datasets import load_dataset
  from PIL import Image
  from transformers import Pipeline
  from transformers.pipelines import get_supported_tasks
  from transformers.pipelines import get_task
  from transformers.testing_utils import nested_simplify
  from transformers.trainer_utils import set_seed
  from transformers.utils import is_tf_available
  from transformers.utils import is_torch_available
  import bentoml
  from bentoml._internal.frameworks.transformers import SimpleDefaultMapping
  from bentoml._internal.frameworks.transformers import TaskDefinition
  from bentoml._internal.frameworks.transformers import convert_to_autoclass
  from bentoml._internal.frameworks.transformers import delete_pipeline
  from bentoml._internal.frameworks.transformers import register_pipeline
  from .None import FrameworkTestModel
  from .None import FrameworkTestModelConfiguration
  from .None import FrameworkTestModelInput
label_desc: Use image node_id 6 for calling a global function with example usage: pretrained_model() and returns: getattr