class_docstr: NCM Classifier.
  NCMClassifier performs nearest class mean classification
  measuring the distance between the input tensor and the
  ones stored in 'self.class_means'.
  
  Before being used for inference, NCMClassifier needs to
  be updated with a mean vector per class, by calling
  `update_class_means_dict`.
  
  This class registers a `class_means` buffer that stores
  the class means in a single tensor of shape
  [max_class_id_seen, feature_size]. Classes with ID smaller
  than `max_class_id_seen` are associated with a 0-vector.
code: def init_missing_classes(self, classes, class_size, device):
      for k in classes:
          if k not in self.class_means_dict:
              self.class_means_dict[k] = torch.zeros(class_size).to(
                  device
              )
      self._vectorize_means_dict()
  
file_path: avalanche/avalanche/models/ncm_classifier.py
imports: from typing import Dict
  import torch
  from torch import Tensor, nn
  from avalanche.models import DynamicModule
label_desc: Use image node_id 7 for calling the NCMClassifier obj's underlying member method code with example usage: obj.init_missing_classes(classes, class_size, device) without return types