code: def softmax(outputs: NDArray[t.Any]) -> NDArray[t.Any]:
      maxes = np.max(outputs, axis=-1, keepdims=True)
      shifted_exp = np.exp(outputs - maxes)
      return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
  
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 1 for calling a global function with example usage: softmax(outputs) and returns: unknown