code: def _create_simple_model():
      class Net(nn.Module):
          def __init__(self):
              super().__init__()
              self.conv1 = nn.Conv2d(3, 6, 5)
              self.pool = nn.MaxPool2d(2, 2)
              self.conv2 = nn.Conv2d(6, 16, 5)
              self.fc1 = nn.Linear(16 * 5 * 5, 120)
              self.fc2 = nn.Linear(120, 84)
              self.fc3 = nn.Linear(84, 10)
  
          def forward(self, x):
              x = self.pool(F.relu(self.conv1(x)))
              x = self.pool(F.relu(self.conv2(x)))
              x = torch.flatten(
                  x, 1
              )  # flatten all dimensions except batch
              x = F.relu(self.fc1(x))
              x = F.relu(self.fc2(x))
              x = self.fc3(x)
              return x
  
      return Net()
  
imports: import copy
  import pytest
  import torch
  import torch.functional
  import torch.nn
  from coremltools.optimize.torch.palettization import DKMPalettizer, DKMPalettizerConfig
  from coremltools.optimize.torch.palettization.palettization_config import DEFAULT_PALETTIZATION_SCHEME
  from coremltools.test.optimize.torch.palettization.palettization_utils import _assert_changes_post_attach, _assert_changes_post_prepare
label_desc: Use image node_id 1 for calling a global function with example usage: _create_simple_model() and returns: Net, x