code: def test_EncoderNormalizer(kwargs):
      kwargs.setdefault("method", "standard")
      kwargs.setdefault("center", True)
      kwargs.setdefault("data", torch.rand(100))
      data = kwargs.pop("data")
  
      normalizer = EncoderNormalizer(**kwargs)
  
      if kwargs.get("transformation") in ["relu", "softplus", "log1p"]:
          assert (
              normalizer.inverse_transform(
                  torch.as_tensor(normalizer.fit_transform(data))
              )
              >= 0
          ).all(), "Inverse transform should yield only positive values"
      else:
          assert torch.isclose(
              normalizer.inverse_transform(
                  torch.as_tensor(normalizer.fit_transform(data))
              ),
              torch.as_tensor(data),
              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 3 for calling a global function with example usage: test_EncoderNormalizer(kwargs) without return types