code: def test_CanonicalInstanceSplitter(
      start,
      target,
      is_train: bool,
      use_prediction_features: bool,
      allow_target_padding: bool,
  ):
      train_length = 100
      pred_length = 13
      t = transform.CanonicalInstanceSplitter(
          target_field=FieldName.TARGET,
          is_pad_field=FieldName.IS_PAD,
          start_field=FieldName.START,
          forecast_start_field=FieldName.FORECAST_START,
          instance_sampler=(
              transform.UniformSplitSampler(
                  p=1.0,
                  min_past=train_length,
              )
              if is_train
              else (
                  transform.ValidationSplitSampler()
                  if allow_target_padding
                  else transform.TestSplitSampler()
              )
          ),
          instance_length=train_length,
          prediction_length=pred_length,
          time_series_fields=["some_time_feature"],
          allow_target_padding=allow_target_padding,
          use_prediction_features=use_prediction_features,
      )
  
      assert_serializable(t)
  
      other_feat = np.arange(len(target) + 100)
      data = {
          "start": start,
          "target": target,
          "some_time_feature": other_feat,
          "some_other_col": "ABC",
      }
  
      out = list(t.flatmap_transform(data, is_train=is_train))
  
      min_num_instances = (
          1 if allow_target_padding and not is_train else 0
      )
      if is_train:
          assert len(out) == max(
              min_num_instances, len(target) - train_length + 1
          )
      else:
          assert len(out) == 1
  
      for o in out:
          assert "target" not in o
          assert "future_target" not in o
          assert "some_time_feature" not in o
          assert "some_other_col" in o
  
          assert len(o["past_some_time_feature"]) == train_length
          assert len(o["past_target"]) == train_length
  
          if use_prediction_features and not is_train:
              assert len(o["future_some_time_feature"]) == pred_length
  
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 7 for calling a global function with example usage: test_CanonicalInstanceSplitter(start, target, is_train, use_prediction_features, allow_target_padding) without return types