code: def test_numeric(self):
      mlp = MLP(mlp_sizes).cuda()
  
      mlp_layers = []
      for i in range(mlp.num_layers):
          linear = nn.Linear(mlp_sizes[i], mlp_sizes[i + 1])
          with torch.no_grad():
              mlp.weights[i].copy_(linear.weight)
              mlp.biases[i].copy_(linear.bias)
          mlp_layers.append(linear)
          mlp_layers.append(nn.ReLU())
  
      ref_mlp = nn.Sequential(*mlp_layers).cuda()
  
      test_input = (
          torch.empty(batch_size, mlp_sizes[0], device="cuda")
          .uniform_(-1.0, 1.0)
          .requires_grad_()
      )
      ref_input = test_input.clone().detach().requires_grad_()
      mlp_out = mlp(test_input)
      ref_out = ref_mlp(ref_input)
      self.assertEqual(mlp_out, ref_out)
  
      # Use mean value as scalar loss. Multiply 10 to make it big enough not zero out
      mlp_out.mean().mul(10.0).backward()
      ref_out.mean().mul(10.0).backward()
      self.assertEqual(test_input.grad, ref_input.grad)
      self.assertEqual(mlp.biases[0].grad, ref_mlp[0].bias.grad)
  
file_path: apex/tests/L0/run_mlp/test_mlp.py
imports: from itertools import product
  from time import time
  import torch
  from torch import nn
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_device_type import instantiate_device_type_tests
  from torch.testing._internal.common_device_type import onlyCUDA
  from apex.mlp import MLP
label_desc: Use image node_id 2 for calling the TestMLP obj's underlying member method code with example usage: obj.test_numeric() without return types