code: def __getitem__(
      self, item: Union[int, List[int], np.ndarray]
  ) -> TArrayDict:
      batch = {k: self.x[k][item] for k in self.x_keys}
      if self.y is not None:
          label = self.y[item]
          batch.update({LABEL_KEY: label, ORIGINAL_LABEL_KEY: label})
      batch = self.processor.postprocess_item(batch)
      return batch
  
imports: import numpy
  from abc import abstractmethod
  from typing import List
  from typing import Union
  from typing import Optional
  from cftool.types import arr_type
  from cftool.types import np_dict_type
  from .utils import TArrayDict
  from .utils import IArrayDataset
  from .utils import IArrayDictDataset
  from .utils import IArrayDataMixin
  from ..schema import IData
  from ..schema import DataArgs
  from ..schema import DataProcessor
  from ..constants import INPUT_KEY
  from ..constants import LABEL_KEY
  from ..constants import ORIGINAL_LABEL_KEY
  from ..misc.toolkit import tensor_batch_to_np
label_desc: Use image node_id 3 for calling the ArrayDictDataset obj's underlying member method code with example usage: obj.__getitem__(item) and returns: batch