code: def load_breast_cancer(*, return_X_y=False, as_frame=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
      =================   ==============
  
      The copy of UCI ML Breast Cancer Wisconsin (Diagnostic) dataset is
      downloaded from:
      https://archive.ics.uci.edu/dataset/17/breast+cancer+wisconsin+diagnostic
  
      Read more in the :ref:`User Guide <breast_cancer_dataset>`.
  
      Parameters
      ----------
      return_X_y : bool, 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
  
      as_frame : bool, default=False
          If True, the data is a pandas DataFrame including columns with
          appropriate dtypes (numeric). The target is
          a pandas DataFrame or Series depending on the number of target columns.
          If `return_X_y` is True, then (`data`, `target`) will be pandas
          DataFrames or Series as described below.
  
          .. versionadded:: 0.23
  
      Returns
      -------
      data : :class:`~sklearn.utils.Bunch`
          Dictionary-like object, with the following attributes.
  
          data : {ndarray, dataframe} of shape (569, 30)
              The data matrix. If `as_frame=True`, `data` will be a pandas
              DataFrame.
          target : {ndarray, Series} of shape (569,)
              The classification target. If `as_frame=True`, `target` will be
              a pandas Series.
          feature_names : ndarray of shape (30,)
              The names of the dataset columns.
          target_names : ndarray of shape (2,)
              The names of target classes.
          frame : DataFrame of shape (569, 31)
              Only present when `as_frame=True`. DataFrame with `data` and
              `target`.
  
              .. versionadded:: 0.23
          DESCR : str
              The full description of the dataset.
          filename : str
              The path to the location of the data.
  
              .. versionadded:: 0.20
  
      (data, target) : tuple if ``return_X_y`` is True
          A tuple of two ndarrays by default. The first contains a 2D ndarray of
          shape (569, 30) with each row representing one sample and each column
          representing the features. The second ndarray of shape (569,) contains
          the target samples.  If `as_frame=True`, both arrays are pandas objects,
          i.e. `X` a dataframe and `y` a series.
  
          .. versionadded:: 0.18
  
      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']
      """
      data_file_name = "breast_cancer.csv"
      data, target, target_names, fdescr = load_csv_data(
          data_file_name=data_file_name,
          descr_file_name="breast_cancer.rst",
      )
  
      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",
          ]
      )
  
      frame = None
      target_columns = [
          "target",
      ]
      if as_frame:
          frame, data, target = _convert_data_dataframe(
              "load_breast_cancer",
              data,
              target,
              feature_names,
              target_columns,
          )
  
      if return_X_y:
          return data, target
  
      return Bunch(
          data=data,
          target=target,
          frame=frame,
          target_names=target_names,
          DESCR=fdescr,
          feature_names=feature_names,
          filename=data_file_name,
          data_module=DATA_MODULE,
      )
  
imports: import csv
  import gzip
  import hashlib
  import os
  import shutil
  from collections import namedtuple
  from numbers import Integral
  from os import environ, listdir, makedirs
  from os.path import expanduser, isdir, join, splitext
  from pathlib import Path
  from urllib.request import urlretrieve
  import numpy
  from ..preprocessing import scale
  from ..utils import Bunch, check_pandas_support, check_random_state
  from ..utils._param_validation import Interval, StrOptions, validate_params
  from ..utils.fixes import _contents, _open_binary, _open_text, _read_text
label_desc: Use image node_id 10 for calling a global function with example usage: load_breast_cancer() and returns: Bunch, data,  target