code: def call(self, *args: tf.Tensor):
      inputs = {
          key: value
          for key, value in zip(self.inputs_types.keys(), args[0])
      }
      outputs = self.core_model(**inputs)
      outputs = (
          outputs.values() if isinstance(outputs, dict) else outputs
      )
      return tuple(flatten_outputs(list(outputs)))
  
file_path: nebuly/optimization/nebullvm/nebullvm/tools/huggingface.py
imports: from collections import OrderedDict
  from typing import Union, Iterable, List, Dict, Tuple, Type, Any
  import numpy
  from nebullvm.core.models import Device, DeviceType
  from nebullvm.optional_modules.tensorflow import tensorflow
  from nebullvm.optional_modules.torch import torch, Module
label_desc: Use image node_id 2 for calling the TensorFlowTransformerWrapper obj's underlying member method code with example usage: obj.call() and returns: tuple