code: def load_data_and_predictions(
      data_format: str = "Dataset",
      load_train: bool = True,
      modify_timestamps: bool = True,
      data_size: t.Optional[int] = 15000,
      random_state: int = 42,
  ) -> t.Tuple[t.Union[Dataset, pd.DataFrame], np.ndarray]:
      """Load and returns the Airbnb NYC 2019 dataset (regression).
  
      Parameters
      ----------
      data_format : str , default: Dataset
          Represent the format of the returned value. Can be 'Dataset'|'Dataframe'
          'Dataset' will return the data as a Dataset object
          'Dataframe' will return the data as a pandas Dataframe object
      load_train : bool , default: True
          If True, the returned data is the train data. otherwise the test dataset.
      modify_timestamps : bool , default: True
          If True, the returned data timestamp column will be for the last 30 days.
          Otherwise, the data timestamp will be for March 2023.
      data_size : t.Optional[int] , default: 15000
          The number of samples to return. If None, returns all the data.
      random_state : int , default 42
          The random state to use for sampling.
      Returns
      -------
      dataset, predictions : Tuple[Union[deepchecks.Dataset, pd.DataFrame], np.ndarray]
          Tuple of the deepchecks dataset or dataframe and the predictions.
      """
      if load_train:
          dataset = pd.read_csv(_TRAIN_DATA_URL)
      else:
          dataset = pd.read_csv(_TEST_DATA_URL)
  
      if data_size is not None:
          if data_size < len(dataset):
              dataset = dataset.sample(
                  data_size, random_state=random_state
              )
          elif data_size > len(dataset):
              dataset = pd.concat(
                  [dataset] * math.ceil(data_size / len(dataset)),
                  axis=0,
                  ignore_index=True,
              )
              dataset = dataset.sample(
                  data_size, random_state=random_state
              )
              if not load_train:
                  dataset = dataset.sort_values(_datetime)
  
      if modify_timestamps and not load_train:
          current_time = int(time.time())
          time_test_start = (
              current_time - 86400 * 30
          )  # Span data for 30 days
          dataset[_datetime] = np.sort(
              (
                  np.random.rand(len(dataset))
                  * (current_time - time_test_start)
              )
              + time_test_start
          )
          dataset[_datetime] = dataset[_datetime].apply(
              lambda x: pd.Timestamp(x, unit="s")
          )
  
      predictions = np.asarray(dataset[_predictions])
      dataset.drop(_predictions, axis=1, inplace=True)
      if data_format == "Dataset":
          dataset = Dataset(
              dataset,
              label=_target,
              cat_features=_CAT_FEATURES,
              features=_FEATURES,
          )
      return dataset, predictions
  
file_path: deepchecks/deepchecks/tabular/datasets/regression/airbnb.py
imports: import math
  import time
  import typing
  import numpy
  import pandas
  from deepchecks.tabular.dataset import Dataset
label_desc: Use image node_id 1 for calling a global function with example usage: load_data_and_predictions(data_format, load_train, modify_timestamps, data_size, random_state) and returns: dataset,  predictions