code: def _multitask_same_as_individual_tasks(
      classification_metric, regression_metric, multitask_metrics
  ):
      """Update classification and regression metrics individually and together using a multitask wrapper.
  
      Return True if the results are the same.
  
      """
      classification_metric.update(
          _classification_preds, _classification_target
      )
      regression_metric.update(_regression_preds, _regression_target)
      multitask_metrics.update(_multitask_preds, _multitask_targets)
  
      classification_results = classification_metric.compute()
      regression_results = regression_metric.compute()
      multitask_results = multitask_metrics.compute()
  
      return _dict_results_same_as_individual_results(
          classification_results, regression_results, multitask_results
      )
  
file_path: torchmetrics/tests/unittests/wrappers/test_multitask.py
imports: import re
  import pytest
  import torch
  from torchmetrics import MetricCollection
  from torchmetrics.classification import BinaryAccuracy, BinaryF1Score
  from torchmetrics.regression import MeanAbsoluteError, MeanSquaredError
  from torchmetrics.wrappers import MultitaskWrapper
  from unittests import BATCH_SIZE, NUM_BATCHES
  from unittests.helpers import seed_all
label_desc: Use image node_id 2 for calling a global function with example usage: _multitask_same_as_individual_tasks(classification_metric, regression_metric, multitask_metrics) and returns: _dict_results_same_as_individual_results