code: def __init__(self, device, has_weight=True, has_bias=True):
      super().__init__()
      self.device = device
      self.scale0 = torch.nn.ParameterList(
          [torch.nn.Parameter(torch.randn(10)) for _ in range(5)]
      ).to(self.device)
      self.bias0 = torch.nn.ParameterList(
          [torch.nn.Parameter(torch.randn(10)) for _ in range(5)]
      ).to(self.device)
      self.scale1 = (
          torch.nn.ParameterList(
              [torch.nn.Parameter(torch.randn(5, 10)) for _ in range(5)]
          ).to(self.device)
          if has_weight
          else [None for _ in range(5)]
      )
      self.bias1 = (
          torch.nn.ParameterList(
              [torch.nn.Parameter(torch.randn(5, 10)) for _ in range(5)]
          ).to(self.device)
          if has_bias
          else [None for _ in range(5)]
      )
  
imports: import functools
  import unittest
  import torch
  import torch._inductor
  from torch._dynamo.test_case import run_tests, TestCase
  from torch._dynamo.utils import counters
  from torch.testing._internal.inductor_utils import HAS_CUDA
label_desc: Use image node_id 1 to create a new MyModule3 object from inherited base classes: torch.nn with example: obj = MyModule3(device, has_weight, has_bias)