class_docstr: The ``DatasetInput`` implements default behaviours for data sources which expect the input to
  :meth:`~flash.core.data.io.input.Input.load_data` to be a :class:`torch.utils.data.dataset.Dataset`
code: def load_sample(self, sample: Any) -> Dict[str, Any]:
      if isinstance(sample, tuple) and len(sample) == 2:
          return {DataKeys.INPUT: sample[0], DataKeys.TARGET: sample[1]}
      return {DataKeys.INPUT: sample}
  
imports: import os
  from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
  import numpy
  import pytorch_lightning
  import torch
  from torch.utils.data import DataLoader, Dataset
  from torch.utils.data.dataset import IterableDataset
  from torch.utils.data.sampler import Sampler
  import flash
  from flash.core.data.base_viz import BaseVisualization
  from flash.core.data.callback import BaseDataFetcher
  from flash.core.data.io.input import DataKeys, Input, IterableInput
  from flash.core.data.io.input_transform import InputTransform, create_device_input_transform_processor, create_or_configure_input_transform, create_worker_input_transform_processor
  from flash.core.data.splits import SplitDataset
  from flash.core.data.utils import _STAGES_PREFIX
  from flash.core.utilities.imports import _TOPIC_CORE_AVAILABLE
  from flash.core.utilities.stages import RunningStage
  from flash.core.utilities.types import INPUT_TRANSFORM_TYPE
label_desc: Use image node_id 1 for calling the DatasetInput obj's underlying member method code with example usage: obj.load_sample(sample) and returns: dict, dict