class_docstr: Process and show the image batch and its associated label using matplotlib.
code: def show_load_sample(
      self,
      samples: List[Any],
      running_stage: RunningStage,
      limit_nb_samples: int = None,
      figsize: Tuple[int, int] = (6.4, 4.8),
  ):
      win_title: str = f"{running_stage} - show_load_sample"
      self._show_images_and_labels(
          samples, len(samples), win_title, limit_nb_samples, figsize
      )
  
imports: from typing import Any, Callable, Collection, Dict, List, Optional, Sequence, Tuple, Type, Union
  import numpy
  import pandas
  from torch import Tensor
  from torch.utils.data import Dataset
  from flash.core.data.base_viz import BaseVisualization
  from flash.core.data.callback import BaseDataFetcher
  from flash.core.data.data_module import DataModule, DatasetInput
  from flash.core.data.io.input import DataKeys, Input
  from flash.core.data.io.input_transform import INPUT_TRANSFORM_TYPE
  from flash.core.data.utilities.classification import TargetFormatter
  from flash.core.data.utilities.paths import PATH_TYPE
  from flash.core.integrations.labelstudio.input import LabelStudioImageClassificationInput, _parse_labelstudio_arguments
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _MATPLOTLIB_AVAILABLE, _TOPIC_IMAGE_AVAILABLE, Image, requires
  from flash.core.utilities.stages import RunningStage
  from flash.image.classification.input import ImageClassificationCSVInput, ImageClassificationDataFrameInput, ImageClassificationFiftyOneInput, ImageClassificationFilesInput, ImageClassificationFolderInput, ImageClassificationImageInput, ImageClassificationNumpyInput, ImageClassificationTensorInput
  from flash.image.classification.input_transform import ImageClassificationInputTransform
label_desc: Use image node_id 3 for calling the MatplotlibVisualization obj's underlying member method code with example usage: obj.show_load_sample(samples, running_stage, limit_nb_samples, figsize) without return types