code: def test_MultiNormalizer_fitted():
      data = pd.DataFrame(
          dict(
              a=[1, 1, 2, 2, 3],
              b=[1.1, 1.1, 1.0, 5.0, 1.1],
              c=[1.1, 1.1, 1.0, 5.0, 1.1],
          )
      )
  
      normalizer = MultiNormalizer(
          [GroupNormalizer(groups=["a"]), TorchNormalizer()]
      )
  
      with pytest.raises(NotFittedError):
          check_is_fitted(normalizer)
  
      normalizer.fit(data, data)
  
      try:
          check_is_fitted(normalizer.normalizers[0])
          check_is_fitted(normalizer.normalizers[1])
          check_is_fitted(normalizer)
      except NotFittedError:
          pytest.fail(f"{NotFittedError}")
  
file_path: pytorch-forecasting/tests/test_data/test_encoders.py
imports: from copy import deepcopy
  import itertools
  import numpy
  import pandas
  import pytest
  from sklearn.utils.validation import NotFittedError, check_is_fitted
  import torch
  from pytorch_forecasting.data import EncoderNormalizer, GroupNormalizer, MultiNormalizer, NaNLabelEncoder, TorchNormalizer
label_desc: Use image node_id 6 for calling a global function with example usage: test_MultiNormalizer_fitted() without return types