code: def setUp(self):
      pred_noise = 0.1 * torch.randn(
          (SKLearnMetricsTest.num_examples, 1)
      )
  
      self.regression_pred = torch.randn(
          (SKLearnMetricsTest.num_examples, 1)
      )
      self.regression_true = self.regression_pred + pred_noise
      self.classification_pred = self.regression_pred
      self.classification_true = (
          self.regression_true.sigmoid() > SKLearnMetricsTest.threshold
      )
      self.multiclass_classification_pred = torch.randn(
          (SKLearnMetricsTest.num_examples, 3)
      ).softmax(1)
      self.multiclass_classification_true = (
          self.multiclass_classification_pred.argmax(1)
      )
      self.multiclass_errors_indices = torch.where(
          torch.rand(SKLearnMetricsTest.num_examples) > 0.9
      )[0]
      self.multiclass_classification_true[
          self.multiclass_errors_indices
      ] = torch.randint(3, (len(self.multiclass_errors_indices),))
      self.sample_weight = torch.rand(
          (SKLearnMetricsTest.num_examples, 1)
      )
  
file_path: poutyne/tests/framework/metrics/test_sklearn_metrics.py
imports: from itertools import repeat
  from unittest import TestCase, skipIf
  import numpy
  import torch
  from torch.utils.data import DataLoader
  from poutyne import SKLearnMetrics, TensorDataset
label_desc: Use image node_id 1 for calling the SKLearnMetricsTest obj's underlying member method code with example usage: obj.setUp() without return types