class_docstr: For multi-file ONNX models, such as encoder-decoder models, represents a part of the model.
  It has its own `onnxruntime.InferenceSession`, and can perform a forward pass.
class_docstr_tok: ["For","multi-file","ONNX","models",",","such","as","encoder-decoder","models",",","represents","a","part","of","the","model",".","It","has","its","own","`","onnxruntime.InferenceSession","`",",","and","can","perform","a","forward","pass","."]
code: def __call__(self, *args, **kwargs):
      return self.forward(*args, **kwargs)
  
imports: from abc import abstractmethod
  from typing import TYPE_CHECKING, Dict, Optional, Set, Tuple, Union
  import numpy
  import torch
  from transformers.modeling_outputs import BaseModelOutput, Seq2SeqLMOutput
  from onnxruntime import InferenceSession
  from ..utils import NormalizedConfigManager
  from ..utils.logging import warn_once
  from .utils import get_ordered_input_names, logging
label_desc: Use image node_id 4 for calling the ORTModelPart obj's underlying member method code with example usage: obj.__call__() and returns: self