class_docstr: ImageClassification dataset as DataFrame.
  
  Parameters
  ----------
  data : the input for pd.DataFrame
      The input data.
  classes : list of str, optional
      The class synsets for this dataset, if `None`, it will infer from the data.
  image_column : str, default is 'image'
      The name of the column for image paths.
  label_column : str, default is 'label'
      The name for the label column, leave it as is if no label column is available. Note that
      in such case you won't be able to train with this dataset, but can still visualize the images.
class_docstr_tok: ["ImageClassification","dataset","as","DataFrame",".","Parameters","--","--","--","--","--","data",":","the","input","for","pd.DataFrame","The","input","data",".","classes",":","list","of","str",",","optional","The","class","synsets","for","this","dataset",",","if","`","None","`",",","it","will","infer","from","the","data",".","image_column",":","str",",","default","is","'image'","The","name","of","the","column","for","image","paths",".","label_column",":","str",",","default","is","'label'","The","name","for","the","label","column",",","leave","it","as","is","if","no","label","column","is","available",".","Note","that","in","such","case","you","wo","n't","be","able","to","train","with","this","dataset",",","but","can","still","visualize","the","images","."]
docstr: Display images in dataset.
  
  Parameters
  ----------
  indices : iterable of int, optional
      The image indices to be displayed, if `None`, will generate `nsample` indices.
      If `shuffle` == `True`(default), the indices are random numbers.
  nsample : int, optional
      The number of samples to be displayed.
  ncol : int, optional
      The column size of ploted image matrix.
  shuffle : bool, optional
      If `shuffle` is False, will always sample from the begining.
  resize : int, optional
      The image will be resized to (resize, resize) for better visual experience.
  fontsize : int, optional
      The fontsize for the title
code: def show_images(
      self,
      indices=None,
      nsample=16,
      ncol=4,
      shuffle=True,
      resize=224,
      fontsize=20,
  ):
      r"""Display images in dataset.
  
      Parameters
      ----------
      indices : iterable of int, optional
          The image indices to be displayed, if `None`, will generate `nsample` indices.
          If `shuffle` == `True`(default), the indices are random numbers.
      nsample : int, optional
          The number of samples to be displayed.
      ncol : int, optional
          The column size of ploted image matrix.
      shuffle : bool, optional
          If `shuffle` is False, will always sample from the begining.
      resize : int, optional
          The image will be resized to (resize, resize) for better visual experience.
      fontsize : int, optional
          The fontsize for the title
      """
      df = self.reset_index(drop=True)
      if indices is None:
          if not shuffle:
              indices = range(nsample)
          else:
              indices = list(range(len(df)))
              np.random.shuffle(indices)
              indices = indices[: min(nsample, len(indices))]
      images = [
          cv2.cvtColor(
              cv2.resize(
                  cv2.imread(df.at[idx, df.IMG_COL]),
                  (resize, resize),
                  interpolation=cv2.INTER_AREA,
              ),
              cv2.COLOR_BGR2RGB,
          )
          for idx in indices
          if idx < len(df)
      ]
      titles = None
      if df.LABEL_COL in df.columns:
          if df.classes:
              titles = [
                  df.classes[int(df.at[idx, df.LABEL_COL])]
                  + ": "
                  + str(df.at[idx, df.LABEL_COL])
                  for idx in indices
                  if idx < len(df)
              ]
          else:
              titles = [
                  str(df.at[idx, df.LABEL_COL])
                  for idx in indices
                  if idx < len(df)
              ]
      _show_images(images, cols=ncol, titles=titles, fontsize=fontsize)
  
imports: import logging
  import os
  from pathlib import Path
  import warnings
  import numpy
  import pandas
  import matplotlib.pyplot
  from PIL import Image
  import cv2
  from ..data import is_url, url_data
  from ...utils.filesystem import import_try_install
  from ...utils.bbox import bbox_xywh_to_xyxy, bbox_clip_xyxy
label_desc: Use image node_id 5 for calling the ImageClassificationDataset obj's underlying member method code with example usage: obj.show_images(indices, nsample, ncol, shuffle, resize, fontsize) without return types