class_docstr: 注意，如果显示的图像不全，是matplotlib版本问题
  本例程使用matplotlib-3.2.1(windows and ubuntu)绘制正常
  需要额外安装prettytable库
class_docstr_tok: ["\u6ce8\u610f\uff0c\u5982\u679c\u663e\u793a\u7684\u56fe\u50cf\u4e0d\u5168\uff0c\u662fmatplotlib\u7248\u672c\u95ee\u9898","\u672c\u4f8b\u7a0b\u4f7f\u7528matplotlib-3.2.1","(","windows","and","ubuntu",")","\u7ed8\u5236\u6b63\u5e38","\u9700\u8981\u989d\u5916\u5b89\u88c5prettytable\u5e93"]
code: def __init__(self, num_classes: int, labels: list):
      self.matrix = np.zeros((num_classes, num_classes))
      self.num_classes = num_classes
      self.labels = labels
  
file_path: deep-learning-for-image-processing/pytorch_classification/ConfusionMatrix/main.py
imports: import os
  import json
  import torch
  from torchvision import transforms, datasets
  import numpy
  from tqdm import tqdm
  import matplotlib.pyplot
  from prettytable import PrettyTable
  from model import MobileNetV2
label_desc: Use image node_id 1 to create a new ConfusionMatrix object from inherited base classes: object with example: obj = ConfusionMatrix(num_classes, labels)