code: def get(self, proc_column, idx=None):
      if idx is None:
          idx = range(self.size)
      if (
          self.data_hdf5_fp is None
          or PREPROCESSING not in self.features[proc_column]
          or "in_memory"
          not in self.features[proc_column]["preprocessing"]
      ):
          return self.dataset[proc_column][idx]
      if self.features[proc_column][PREPROCESSING]["in_memory"]:
          return self.dataset[proc_column][idx]
  
      sub_batch = self.dataset[proc_column][idx]
  
      indices = np.empty((3, len(sub_batch)), dtype=np.int64)
      indices[0, :] = sub_batch
      indices[1, :] = np.arange(len(sub_batch))
      indices = indices[:, np.argsort(indices[0])]
  
      with download_h5(self.data_hdf5_fp) as h5_file:
          im_data = h5_file[proc_column + "_data"][indices[0, :], :, :]
      indices[2, :] = np.arange(len(sub_batch))
      indices = indices[:, np.argsort(indices[1])]
      return im_data[indices[2, :]]
  
imports: from __future__ import annotations
  import contextlib
  from typing import Iterable, TYPE_CHECKING
  import numpy
  from pandas import DataFrame
  from ludwig.constants import PREPROCESSING, TRAINING
  from ludwig.data.batcher.base import Batcher
  from ludwig.data.batcher.random_access import RandomAccessBatcher
  from ludwig.data.dataset.base import Dataset, DatasetManager
  from ludwig.data.sampler import DistributedSampler
  from ludwig.distributed import DistributedStrategy
  from ludwig.features.base_feature import BaseFeature
  from ludwig.utils.data_utils import DATA_TRAIN_HDF5_FP, load_hdf5, save_hdf5
  from ludwig.utils.dataframe_utils import from_numpy_dataset, to_numpy_dataset, to_scalar_df
  from ludwig.utils.defaults import default_random_seed
  from ludwig.utils.fs_utils import download_h5
  from ludwig.utils.misc_utils import get_proc_features
label_desc: Use image node_id 4 for calling the PandasDataset obj's underlying member method code with example usage: obj.get(proc_column, idx) and returns: im_data, self, self