docstr: In the forward function we accept a Tensor of input data
docstr_tok: ["In","the","forward","function","we","accept","a","Tensor","of","input","data"]
code: def forward(self, x):
      """
      In the forward function we accept a Tensor of input data
      """
      h_relu = self.linear1(x).clamp(min=0)
      y_pred = self.linear2(h_relu)
      return y_pred
  
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 2 for calling the PyTorchModel obj's underlying member method code with example usage: obj.forward(x) and returns: y_pred