code: def forward(self, x):
      y = torch.matmul(
          self.p, torch.randn(100, 100, device=self.p.device)
      )
      return torch.matmul(x, y)
  
imports: import unittest
  from collections import deque, OrderedDict
  from contextlib import ContextDecorator
  from copy import deepcopy
  from typing import Tuple
  import torch
  import torch.nn
  from torch.distributed._composable import checkpoint
  from torch.testing._internal.common_cuda import TEST_CUDA
  from torch.testing._internal.common_utils import run_tests, TestCase
label_desc: Use image node_id 2 for calling the RandomModel obj's underlying member method code with example usage: obj.forward(x) and returns: torch