code: def __init__(self, z, device, has_bias):
      super().__init__()
      self.z = z
      self.device = device
      self.has_bias = has_bias
      self.seq_len = 10
      self.weights1 = [
          torch.nn.Parameter(torch.randn(z - i % 5, z)).to(self.device)
          for i in range(self.seq_len)
      ]
      self.weights2 = [
          torch.nn.Parameter(torch.randn(z - i % 5, z)).to(self.device)
          for i in range(self.seq_len)
      ]
  
      if has_bias:
          self.biases1 = [
              torch.nn.Parameter(torch.randn(z - i % 5)).to(self.device)
              for i in range(self.seq_len)
          ]
          self.biases2 = [
              torch.nn.Parameter(torch.randn(z - i % 5)).to(self.device)
              for i in range(self.seq_len)
          ]
  
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 MyModule4 object from inherited base classes: torch.nn with example: obj = MyModule4(z, device, has_bias)