code: def module(get_circuit, n_qubits, output_dim):
      """Fixture for creating a hybrid Torch module. The module is composed of quantum TorchLayers
      sandwiched between Linear layers."""
      c, w = get_circuit
  
      class Net(
          torch.nn.Module
      ):  # pylint: disable=too-few-public-methods
          """Neural network."""
  
          def __init__(self):
              super().__init__()
              self.clayer1 = torch.nn.Linear(n_qubits, n_qubits)
              self.clayer2 = torch.nn.Linear(output_dim, n_qubits)
              self.clayer3 = torch.nn.Linear(output_dim, output_dim)
              self.qlayer1 = TorchLayer(c, w)
              self.qlayer2 = TorchLayer(c, w)
  
          def forward(self, x):
              """Forward pass."""
              x = self.clayer1(x)
              x = self.qlayer1(x)
              x = self.clayer2(x)
              x = self.qlayer2(x)
              x = self.clayer3(x)
              return x
  
      return Net()
  
imports: from unittest import mock
  import math
  from collections import defaultdict
  import numpy
  import pytest
  import pennylane
  from pennylane.qnn.torch import TorchLayer
label_desc: Use image node_id 2 for calling a global function with example usage: module(get_circuit, n_qubits, output_dim) and returns: Net, x