class_docstr: Test dataset windowing with different dataset sizes and settings.
  
  Note that for these tests to run efficiently, windowing must be triggered while remaining within the object store
  memory size. The current heuristic is to trigger windowing when the dataset exceeds
  `ray.cluster_resources()['object_store_memory'] // 5` bytes.
class_docstr_tok: ["Test","dataset","windowing","with","different","dataset","sizes","and","settings",".","Note","that","for","these","tests","to","run","efficiently",",","windowing","must","be","triggered","while","remaining","within","the","object","store","memory","size",".","The","current","heuristic","is","to","trigger","windowing","when","the","dataset","exceeds","`","ray.cluster_resources","(",")","[","'object_store_memory","'","]","\/\/","5","`","bytes","."]
docstr: The amount of object store memory available to the cluster fixture.
code: def object_store_size(self):
      """The amount of object store memory available to the cluster fixture."""
      return int(ray.cluster_resources()["object_store_memory"])
  
imports: import copy
  import os
  import tempfile
  from typing import Literal, Optional, Union
  import numpy
  import pandas
  import pytest
  import torch
  from ludwig.api import LudwigModel
  from ludwig.backend import create_ray_backend, initialize_backend, LOCAL_BACKEND
  from ludwig.constants import AUDIO, BAG, BALANCE_PERCENTAGE_TOLERANCE, BATCH_SIZE, BFILL, BINARY, CATEGORY, COLUMN, DATE, H3, IMAGE, MAX_BATCH_SIZE_DATASET_FRACTION, NAME, NUMBER, PREPROCESSING, SEQUENCE, SET, SPLIT, TEXT, TIMESERIES, TRAINER, VECTOR
  from ludwig.data.preprocessing import balance_data
  from ludwig.data.split import DEFAULT_PROBABILITIES
  from ludwig.utils.data_utils import read_parquet
  from ludwig.utils.misc_utils import merge_dict
  from tests.integration_tests.utils import audio_feature, augment_dataset_with_none, bag_feature, binary_feature, category_feature, create_data_set_to_use, date_feature, generate_data, h3_feature, image_feature, number_feature, RAY_BACKEND_CONFIG, sequence_feature, set_feature, text_feature, timeseries_feature, train_with_backend, vector_feature
  import dask
  import ray
  import ray.exceptions
  from ray.air.config import DatasetConfig
  from ray.data import Dataset, DatasetPipeline
  from ray.train._internal.dataset_spec import DataParallelIngestSpec
  from ludwig.backend.ray import get_trainer_kwargs, RayBackend
  from ludwig.data.dataframe.dask import DaskEngine
label_desc: Use image node_id 1 for calling the TestDatasetWindowAutosizing obj's underlying member method code with example usage: obj.object_store_size() and returns: int