docstr: In the constructor we instantiate two nn.Linear modules and assign them as
  member variables.
docstr_tok: ["In","the","constructor","we","instantiate","two","nn.Linear","modules","and","assign","them","as","member","variables","."]
code: def __init__(self, D_in, H, D_out):
      """
      In the constructor we instantiate two nn.Linear modules and assign them as
      member variables.
      """
      super(PyTorchModel, self).__init__()
      self.linear1 = nn.Linear(D_in, H)
      self.linear2 = nn.Linear(H, D_out)
  
imports: from __future__ import annotations
  import os
  import tempfile
  import typing
  from typing import TYPE_CHECKING
  import numpy
  import onnx
  import onnxruntime
  import sklearn
  import torch
  import torch.nn
  from skl2onnx import convert_sklearn
  from skl2onnx.common.data_types import FloatTensorType
  from skl2onnx.common.data_types import Int64TensorType
  from skl2onnx.common.data_types import StringTensorType
  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import LabelEncoder
  import bentoml
  from .None import FrameworkTestModel
  from .None import FrameworkTestModelConfiguration
  from .None import FrameworkTestModelInput
label_desc: Use image node_id 1 to create a new PyTorchModel object from inherited base classes: nn with example: obj = PyTorchModel(D_in, H, D_out)