code: def __init__(
      self,
      core_model: tf.Module,
      encoded_input: Dict[str, tf.Tensor],
  ):
      super().__init__()
      self.core_model = core_model
      self.inputs_types = OrderedDict()
      for key, value in encoded_input.items():
          self.inputs_types[key] = value.dtype
  
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 1 to create a new TensorFlowTransformerWrapper object from inherited base classes: tf.keras with example: obj = TensorFlowTransformerWrapper(core_model, encoded_input)