code: def classification_report(
      y_true,
      y_pred,
      *,
      target_names=None,
      sample_weight=None,
      zero_division=0,
      ax=None,
  ):
      """Classification report
  
      Parameters
      ----------
      y_true : array-like, shape = [n_samples]
          Correct target values (ground truth)
  
      y_pred : array-like, shape = [n_samples]
          Target predicted classes (estimator predictions)
  
      target_names : list
          List containing the names of the target classes. List must be in order
          e.g. ``['Label for class 0', 'Label for class 1']``. If ``None``,
          generic labels will be generated e.g. ``['Class 0', 'Class 1']``
  
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights.
  
      zero_division : bool,  0 or 1
          Sets the value to return when there is a zero division.
  
      ax : matplotlib Axes
          Axes object to draw the plot onto, otherwise uses current Axes
  
      Returns
      -------
      ax: matplotlib Axes
          Axes containing the plot
  
      Examples
      --------
      .. plot:: ../examples/classification_report.py
  
      .. plot:: ../examples/classification_report_multiclass.py
      """
  
      if ax is None:
          ax = plt.gca()
  
      matrix, keys, target_names = _classification_report(
          y_true,
          y_pred,
          target_names=target_names,
          sample_weight=sample_weight,
          zero_division=zero_division,
      )
  
      return _classification_report_plot(matrix, keys, target_names, ax)
  
imports: from warnings import warn
  from pathlib import Path
  import json
  import numpy
  import matplotlib.pyplot
  from sklearn.metrics import classification_report
  from ..telemetry import SKLearnEvaluationLogger
  from sklearn_evaluation.plot.classification import _add_values_to_matrix
  from sklearn_evaluation.util import default_heatmap
  from sklearn_evaluation.plot.plot import Plot
  from sklearn_evaluation.plot import _matrix
  from sklearn_evaluation import __version__
  from ploomber_core.exceptions import modify_exceptions
label_desc: Use image node_id 4 for calling a global function with example usage: classification_report(y_true, y_pred) and returns: _classification_report_plot