code: def __init__(self):
      super().__init__()
  
      self.conv = torch.nn.Conv2d(3, 32, kernel_size=(3, 3))
      self.activation = torch.nn.ReLU()
      self.max_pool = torch.nn.MaxPool2d((2, 2))
      self.flatten = torch.nn.Flatten()
      self.dense = torch.nn.LazyLinear(num_classes)
      self.softmax = torch.nn.Softmax(dim=1)
  
file_path: keras/benchmarks/torch_ctl_benchmark/conv_model_benchmark.py
imports: import numpy
  import torch
  import torch.nn
  import torch.optim
  import keras
  from benchmarks.torch_ctl_benchmark.benchmark_utils import train_loop
  from keras import layers
label_desc: Use image node_id 1 to create a new TorchModel object from inherited base classes: torch.nn with example: obj = TorchModel()