class_docstr: Class for wrappering the Transformers and give them an API compatible
  with nebullvm. The class takes and input of the forward method positional
  arguments and transform them in the input dictionaries needed by
  transformers classes. At the end it also flattens their output.
class_docstr_tok: ["Class","for","wrappering","the","Transformers","and","give","them","an","API","compatible","with","nebullvm",".","The","class","takes","and","input","of","the","forward","method","positional","arguments","and","transform","them","in","the","input","dictionaries","needed","by","transformers","classes",".","At","the","end","it","also","flattens","their","output","."]
code: def __init__(
      self,
      core_model: Module,
      encoded_input: Dict[str, torch.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 PyTorchTransformerWrapper object from inherited base classes: Module with example: obj = PyTorchTransformerWrapper(core_model, encoded_input)