code: def _confusion_matrix(y_true, y_pred, normalize):
      cm = sk_confusion_matrix(y_true, y_pred)
  
      if normalize:
          cm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
  
      return cm
  
imports: import json
  from pathlib import Path
  import numpy
  import matplotlib.pyplot
  from sklearn.metrics import confusion_matrix
  from sklearn_evaluation import __version__
  from ..telemetry import SKLearnEvaluationLogger
  from ..plot.matplotlib import bar
  from ..metrics import precision_at
  from ..None import compute
  from ..util import is_column_vector, is_row_vector, default_heatmap
  from sklearn_evaluation.plot.plot import AbstractPlot, AbstractComposedPlot
  from ..plot import _matrix
  from ploomber_core.exceptions import modify_exceptions
label_desc: Use image node_id 1 for calling a global function with example usage: _confusion_matrix(y_true, y_pred, normalize) and returns: cm