class_docstr: Biological 8-class classification problem.
code: def _info(self):
      return tfds.core.DatasetInfo(
          builder=self,
          description=(
              "Classification of textures in colorectal cancer histology. "
              "Each example is a 150 x 150 x 3 RGB image of one of 8 classes."
          ),
          features=tfds.features.FeaturesDict(
              {
                  "image": tfds.features.Image(
                      shape=(_TILES_SIZE,) * 2 + (3,)
                  ),
                  "label": tfds.features.ClassLabel(
                      names=_CLASS_NAMES,
                      doc=(
                          "Eight classes: "
                          "0: 'tumour epithelium', "
                          "1: 'simple stroma', "
                          "2: 'complex stroma' (stroma that contains single tumour "
                          "cells and/or single immune cells), "
                          "3: 'immune cell conglomerates', "
                          "4: 'debris and mucus', "
                          "5: 'mucosal glands', "
                          "6: 'adipose tissue', and "
                          "7: 'background'."
                      ),
                  ),
                  "filename": tfds.features.Text(),
              }
          ),
          homepage=_URL,
          citation=_CITATION,
          supervised_keys=("image", "label"),
      )
  
file_path: datasets/tensorflow_datasets/image_classification/colorectal_histology.py
imports: import os
  import numpy
  from tensorflow_datasets.core.utils.lazy_imports_utils import tensorflow
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling the ColorectalHistology obj's underlying member method code with example usage: obj._info() and returns: tfds