code: def load_breast_cancer(return_X_y=False):
      """Load and return the breast cancer wisconsin dataset (classification).
  
      The breast cancer dataset is a classic and very easy binary classification
      dataset.
  
      =================   ==============
      Classes                          2
      Samples per class    212(M),357(B)
      Samples total                  569
      Dimensionality                  30
      Features            real, positive
      =================   ==============
  
      Parameters
      ----------
      return_X_y : boolean, default=False
          If True, returns ``(data, target)`` instead of a Bunch object.
          See below for more information about the `data` and `target` object.
  
          .. versionadded:: 0.18
  
      Returns
      -------
      data : Bunch
          Dictionary-like object, the interesting attributes are:
          'data', the data to learn, 'target', the classification labels,
          'target_names', the meaning of the labels, 'feature_names', the
          meaning of the features, and 'DESCR', the
          full description of the dataset.
  
      (data, target) : tuple if ``return_X_y`` is True
  
          .. versionadded:: 0.18
  
      The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
      downloaded from:
      https://goo.gl/U2Uwz2
  
      Examples
      --------
      Let's say you are interested in the samples 10, 50, and 85, and want to
      know their class name.
  
      >>> from sklearn.datasets import load_breast_cancer
      >>> data = load_breast_cancer()
      >>> data.target[[10, 50, 85]]
      array([0, 1, 0])
      >>> list(data.target_names)
      ['malignant', 'benign']
      """
      module_path = datasets_dir()
      with open(
          join(module_path, "data", "breast_cancer.csv")
      ) as csv_file:
          data_file = csv.reader(csv_file)
          first_line = next(data_file)
          n_samples = int(first_line[0])
          n_features = int(first_line[1])
          target_names = np.array(first_line[2:4])
          data = np.empty((n_samples, n_features))
          target = np.empty((n_samples,), dtype=np.int)
  
          for count, value in enumerate(data_file):
              data[count] = np.asarray(value[:-1], dtype=np.float64)
              target[count] = np.asarray(value[-1], dtype=np.int)
  
      with open(
          join(module_path, "descr", "breast_cancer.rst")
      ) as rst_file:
          fdescr = rst_file.read()
  
      feature_names = np.array(
          [
              "mean radius",
              "mean texture",
              "mean perimeter",
              "mean area",
              "mean smoothness",
              "mean compactness",
              "mean concavity",
              "mean concave points",
              "mean symmetry",
              "mean fractal dimension",
              "radius error",
              "texture error",
              "perimeter error",
              "area error",
              "smoothness error",
              "compactness error",
              "concavity error",
              "concave points error",
              "symmetry error",
              "fractal dimension error",
              "worst radius",
              "worst texture",
              "worst perimeter",
              "worst area",
              "worst smoothness",
              "worst compactness",
              "worst concavity",
              "worst concave points",
              "worst symmetry",
              "worst fractal dimension",
          ]
      )
  
      if return_X_y:
          return data, target
  
      return Bunch(
          data=data,
          target=target,
          target_names=target_names,
          DESCR=fdescr,
          feature_names=feature_names,
      )
  
imports: import os
  import csv
  import sys
  import shutil
  from os import environ
  from os.path import dirname
  from os.path import join
  from os.path import exists
  from os.path import expanduser
  from os.path import isdir
  from os.path import splitext
  from os import listdir
  from os import makedirs
  import numpy
  from ..utils import check_random_state
label_desc: Use image node_id 6 for calling a global function with example usage: load_breast_cancer(return_X_y) and returns: Bunch, data,  target