code: def test(backend):
      # Example code from https://coremltools.readme.io/docs/model-scripting
  
      class _LoopBody(nn.Module):
          def __init__(self, channels):
              super(_LoopBody, self).__init__()
              conv = nn.Conv2d(
                  in_channels=channels,
                  out_channels=channels,
                  kernel_size=3,
                  padding=1,
              )
              self.conv = conv
  
          def forward(self, x):
              x = self.conv(x)
              x = F.relu(x)
              return x
  
      class ControlFlowNet(nn.Module):
          def __init__(self, num_channels: int):
              super(ControlFlowNet, self).__init__()
              self.loop_body = _LoopBody(num_channels)
  
          def forward(self, x):
              avg = torch.mean(x)
              if avg.item() < 0:
                  loop_count = 2
              else:
                  loop_count = 1
              for _ in range(loop_count):
                  x = self.loop_body(x)
              return x
  
      model = ControlFlowNet(num_channels=3)
      scripted_model = torch.jit.script(model)
  
      mlmodel = coremltools.converters.convert(
          scripted_model,
          inputs=[coremltools.TensorType(shape=(1, 3, 64, 64))],
          convert_to=backend[0],
      )
  
imports: import pytest
  import coremltools
  from coremltools._deps import _HAS_TORCH, MSG_TORCH_NOT_FOUND
  from coremltools.converters.mil.testing_reqs import backends
label_desc: Use image node_id 1 for calling the TestModelScripting obj's underlying member method code with example usage: obj.test(backend) and returns: x, x