code: def test_cdf_to_gaussian_transformation():
      def make_test_data():
          target = [
              0,
              0,
              0,
              0,
              10,
              10,
              20,
              20,
              30,
              30,
              40,
              50,
              59,
              60,
              60,
              70,
              80,
              90,
              100,
          ]
          np.random.shuffle(target)
  
          multi_dim_target = np.array([target, target]).transpose()
          past_is_pad = np.atleast_2d(np.zeros_like(target)).transpose()
  
          past_observed_target = np.array(
              [np.ones_like(target), np.ones_like(target)]
          ).transpose()
  
          ds = gluonts.dataset.common.ListDataset(
              # Mimic output from InstanceSplitter
              [
                  {
                      "start": "2012-01-01",
                      "target": multi_dim_target,
                      "past_target": multi_dim_target,
                      "future_target": multi_dim_target,
                      "past_is_pad": past_is_pad,
                      f"past_{FieldName.OBSERVED_VALUES}": past_observed_target,
                  }
              ],
              freq="1D",
              one_dim_target=False,
          )
          return ds
  
      def make_fake_output(u: DataEntry):
          return np.expand_dims(
              np.expand_dims(u["past_target_cdf"], axis=0), axis=0
          )
  
      ds = make_test_data()
  
      t = transform.CDFtoGaussianTransform(
          target_field=FieldName.TARGET,
          observed_values_field=FieldName.OBSERVED_VALUES,
          max_context_length=20,
          target_dim=2,
      )
  
      for u in t(iter(ds), is_train=False):
          fake_output = make_fake_output(u)
  
          # Fake transformation chain output
          u["past_target_sorted"] = np.expand_dims(
              u["past_target_sorted"], axis=0
          )
          u["slopes"] = np.expand_dims(u["slopes"], axis=0)
          u["intercepts"] = np.expand_dims(u["intercepts"], axis=0)
  
          back_transformed = (
              transform.cdf_to_gaussian_forward_transform(
                  u, fake_output
              )
          )
  
          # Get any sample/batch (slopes[i][:, d]they are all the same)
          back_transformed = back_transformed[0][0]
  
          original_target = u["target"]
  
          # Original target and back-transformed target should be the same
          assert np.allclose(original_target, back_transformed)
  
imports: from typing import Tuple, List
  import numpy
  import pandas
  from pandas.tseries.frequencies import to_offset
  import pytest
  import gluonts
  from gluonts import time_feature, transform
  from gluonts.core import fqname_for
  from gluonts.core.serde import dump_json, load_json
  from gluonts.dataset.common import DataEntry, ListDataset
  from gluonts.dataset.field_names import FieldName
  from gluonts.dataset.stat import ScaleHistogram, calculate_dataset_statistics
  from gluonts.transform import CausalMeanValueImputation, DummyValueImputation, LastValueImputation, MeanValueImputation, RollingMeanValueImputation, Valmap
  from gluonts.transform.convert import ToIntervalSizeFormat, erf, erfinv
  from gluonts.transform.feature import CountTrailingZeros
label_desc: Use image node_id 12 for calling a global function with example usage: test_cdf_to_gaussian_transformation() and returns: ds, np