code: def test_GroupNormalizer(kwargs, groups):
      data = pd.DataFrame(
          dict(a=[1, 1, 2, 2, 3], b=[1.1, 1.1, 1.0, 0.0, 1.1])
      )
      defaults = dict(
          method="standard",
          transformation=None,
          center=True,
          scale_by_group=False,
      )
      defaults.update(kwargs)
      kwargs = defaults
      kwargs["groups"] = groups
      kwargs["scale_by_group"] = (
          kwargs["scale_by_group"] and len(kwargs["groups"]) > 0
      )
  
      normalizer = GroupNormalizer(**kwargs)
      encoded = normalizer.fit_transform(data["b"], data)
  
      test_data = dict(
          prediction=torch.tensor([encoded[0]]),
          target_scale=torch.tensor(
              normalizer.get_parameters([1])
          ).unsqueeze(0),
      )
  
      if kwargs.get("transformation") in [
          "relu",
          "softplus",
          "log1p",
          "log",
      ]:
          assert (
              normalizer(test_data) >= 0
          ).all(), "Inverse transform should yield only positive values"
      else:
          assert torch.isclose(
              normalizer(test_data),
              torch.tensor(data.b.iloc[0]),
              atol=1e-5,
          ).all(), "Inverse transform should reverse transform"
  
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 4 for calling a global function with example usage: test_GroupNormalizer(kwargs, groups) without return types