class_docstr: Streaming interface to allow for any source of predictions. 
  Initialize it, count predictions one by one, then print confusion matrix and intersection-union score
class_docstr_tok: ["Streaming","interface","to","allow","for","any","source","of","predictions",".","Initialize","it",",","count","predictions","one","by","one",",","then","print","confusion","matrix","and","intersection-union","score"]
docstr: labels are integers from 0 to number_of_labels-1
docstr_tok: ["labels","are","integers","from","0","to","number_of_labels-1"]
code: def get_count(self, ground_truth, predicted):
      """labels are integers from 0 to number_of_labels-1"""
      return self.confusion_matrix[ground_truth][predicted]
  
file_path: torch-points3d/torch_points3d/metrics/confusion_matrix.py
imports: import numpy
  import torch
  import os
label_desc: Use image node_id 4 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.get_count(ground_truth, predicted) and returns: self