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: Short summary.
  
  Parameters
  ----------
  cls : type
      Description of parameter `cls`.
  im_list : type
      Description of parameter `im_list`.
  fn : type
      Description of parameter `fn`.
  root : type
      Description of parameter `root`.
  
  Returns
  -------
  type
      Description of returned object.
code: def from_name_func(cls, im_list, fn, root=None, no_class=False):
      """Short summary.
  
      Parameters
      ----------
      cls : type
          Description of parameter `cls`.
      im_list : type
          Description of parameter `im_list`.
      fn : type
          Description of parameter `fn`.
      root : type
          Description of parameter `root`.
  
      Returns
      -------
      type
          Description of returned object.
  
      """
      # create from a function parsed from name
      synsets = []
      items = {"image": [], "label": []}
      for im in im_list:
          if isinstance(im, Path):
              path = str(im.resolve())
          else:
              assert isinstance(im, str)
              if root is not None and not os.path.isabs(im):
                  path = os.path.abspath(
                      os.path.join(root, os.path.expanduser(im))
                  )
          items["image"].append(path)
          label = fn(Path(path))
          if isinstance(label, (int, bool, str)):
              label = str(label)
          else:
              raise ValueError(
                  "Expect returned label to be (str, int, bool), received {}".format(
                      type(label)
                  )
              )
          if label not in synsets:
              synsets.append(label)
          items["label"].append(synsets.index(label))  # int label id
      return cls(items, classes=synsets, no_class=no_class)
  
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 11 for calling the ImageClassificationDataset obj's underlying member method code with example usage: obj.from_name_func(cls, im_list, fn, root, no_class) and returns: cls