class_docstr: A splitter that slices training and test data based on a fixed integer
  offset.
  
  Parameters
  ----------
  offset
      Offset determining where the training data ends.
      A positive offset indicates how many observations since the start of
      each series should be in the training slice; a negative offset
      indicates how many observations before the end of each series should
      be excluded from the training slice.
code: def test_pair(
      self, entry: DataEntry, prediction_length: int, offset: int = 0
  ) -> Tuple[DataEntry, DataEntry]:
      offset_ = self.offset + offset
      if self.offset < 0:
          offset_ += entry[FieldName.TARGET].shape[-1]
      assert (
          offset_ + prediction_length
          <= entry[FieldName.TARGET].shape[-1]
      ), "Not enough data to generate some of the windows; try splitting data at an earlier offset"
  
      if offset_ + prediction_length:
          input_slice = slice(None, offset_)
          label_slice = slice(offset_, offset_ + prediction_length)
      else:
          input_slice = slice(None, offset_)
          label_slice = slice(offset_, None)
      return (
          slice_data_entry(
              entry, input_slice, prediction_length=prediction_length
          ),
          slice_data_entry(
              entry, label_slice, prediction_length=prediction_length
          ),
      )
  
imports: from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import Generator, Optional, Tuple
  import pandas
  from gluonts.dataset import Dataset, DataEntry
  from gluonts.dataset.field_names import FieldName
label_desc: Use image node_id 2 for calling the OffsetSplitter obj's underlying member method code with example usage: obj.test_pair(entry, prediction_length, offset) and returns: slice_data_entry,  slice_data_entry