code: def roc_plot(
      target,
      predicted,
      df=pd.DataFrame(),
      cross_cols=[],
      categorical_cols=[],
      bins=6,
      plot=True,
  ):
      return _curve(
          target=target,
          predicted=predicted,
          curve_type="roc",
          df=df,
          cross_cols=cross_cols,
          categorical_cols=categorical_cols,
          bins=bins,
          plot=plot,
      )
  
imports: import pandas
  import matplotlib.pyplot
  import numpy
  from scipy.stats import spearmanr
  from scipy.cluster import hierarchy
  from typing import List, Any, Union, Tuple, Optional, Dict
  import random, math
  from sklearn.metrics import roc_curve, precision_recall_curve, roc_auc_score, confusion_matrix
  import itertools
  import logging
label_desc: Use image node_id 14 for calling a global function with example usage: roc_plot(target, predicted, df, cross_cols, categorical_cols, bins, plot) and returns: _curve