code: def setUp(self):
      torch.manual_seed(42)
      self.pytorch_network = nn.Sequential(
          nn.Linear(1, 1), nn.Flatten(0)
      )
      self.loss_function = nn.MSELoss()
      self.optimizer = torch.optim.SGD(
          self.pytorch_network.parameters(), lr=1e-3
      )
      dataset_size = (
          MetricsModelIntegrationTest.batch_size
          * MetricsModelIntegrationTest.steps_per_epoch
      )
      self.train_x = torch.rand(dataset_size, 1)
      self.train_y = torch.rand(dataset_size)
      self.valid_x = torch.rand(dataset_size, 1)
      self.valid_y = torch.rand(dataset_size)
  
      self.metric_names = ("a", "b")
      self.metric_values = [1, 2]
  
imports: import os
  import unittest
  from unittest import skipIf
  from unittest.mock import ANY
  import numpy
  import torch
  import torch.nn
  import torchmetrics
  from poutyne import Metric, Model, do_register_metric_class, rename_doubles, unregister_metric_class
label_desc: Use image node_id 1 for calling the MetricsModelIntegrationTest obj's underlying member method code with example usage: obj.setUp() without return types