You just need the class membership probabilities for ROC curves. E.g., if you collect the probabilities, you can do this:
import matplotlib.pyplot as plt
import numpy as np


def plot_roc_curve(y_true, y_score, pos_label=1, num_thresholds=100):

    y_true_ary = np.array(y_true)
    y_score_ary = np.array(y_score)
    x_axis_values = []
    y_axis_values = []
    thresholds = np.linspace(0., 1., num_thresholds)

    num_positives = np.sum(y_true == pos_label)
    num_negatives = y_true.shape[0] - num_positives

    for i, thr in enumerate(thresholds):
        
        binarized_scores = np.where(y_score >= thr, pos_label, int(not pos_label))
        
        positive_predictions = (binarized_scores == pos_label)
        num_true_positives = (y_true[positive_predictions] == pos_label).sum()
        num_false_positives = (y_true[positive_predictions] != pos_label).sum()
        
        x_axis_values.append(num_false_positives / float(num_negatives))
        y_axis_values.append(num_true_positives / float(num_positives))

    plt.step(x_axis_values, y_axis_values, where='post')
    
    plt.xlim([0., 1.01])
    plt.ylim([0., 1.01])
    plt.ylabel('True Positive Rate')
    plt.xlabel('False Positive Rate')
    
    return None

plot_roc_curve(y_test, y_probabilities[:, 1], pos_label=1)
plt.show()

(I actually used that as a HW exercise last semester :))