code: def __init__(self):
      super(LeNet, self).__init__()
      self.conv1 = nn.Conv2D(
          in_channels=1,
          out_channels=6,
          kernel_size=5,
          stride=1,
          padding=2,
      )
      self.max_pool1 = nn.MaxPool2D(kernel_size=2, stride=2)
      self.conv2 = nn.Conv2D(
          in_channels=6, out_channels=16, kernel_size=5, stride=1
      )
      self.max_pool2 = nn.MaxPool2D(kernel_size=2, stride=2)
      self.linear1 = nn.Linear(in_features=16 * 5 * 5, out_features=120)
      self.linear2 = nn.Linear(in_features=120, out_features=84)
      self.linear3 = nn.Linear(in_features=84, out_features=10)
  
file_path: PaddleOCR/test_tipc/supplementary/custom_op/test.py
imports: import paddle
  import paddle.nn
  from paddle.vision.transforms import Compose, Normalize
  from paddle.utils.cpp_extension import load
  from paddle.inference import Config
  from paddle.inference import create_predictor
  import numpy
label_desc: Use image node_id 1 to create a new LeNet object from inherited base classes: nn with example: obj = LeNet()