code: def test_ctsplitter_train_correct(point_process_dataset):
      splitter = transform.ContinuousTimeInstanceSplitter(
          past_interval_length=1,
          future_interval_length=1,
          instance_sampler=MockContinuousTimeSampler(
              ret_values=[1.01, 1.5, 1.99]
          ),
          freq=to_offset("H"),
      )
  
      iter_de = splitter(point_process_dataset, is_train=True)
  
      outputs = list(iter_de)
  
      assert outputs[0]["past_valid_length"] == 2
      assert outputs[0]["future_valid_length"] == 3
  
      assert np.allclose(
          outputs[0]["past_target"], np.array([[0.19, 0.7], [0, 1]]).T
      )
      assert np.allclose(
          outputs[0]["future_target"],
          np.array([[0.09, 0.5, 0.3], [2, 0, 1]]).T,
      )
  
      assert outputs[1]["past_valid_length"] == 2
      assert outputs[1]["future_valid_length"] == 4
  
      assert outputs[2]["past_valid_length"] == 3
      assert outputs[2]["future_valid_length"] == 3
  
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 19 for calling a global function with example usage: test_ctsplitter_train_correct(point_process_dataset) without return types