class_docstr: This is a model wrapper to reproduce experiments from the original
  paper of Deep Streaming Linear Discriminant Analysis by using
  a pretrained ResNet model.
docstr: :param x: raw x data
code: def forward(self, x):
      """
      :param x: raw x data
      """
      feat = self.feature_extraction_wrapper(x)
      feat = SLDAResNetModel.pool_feat(feat)
      return feat
  
file_path: avalanche/avalanche/models/slda_resnet.py
imports: from typing import Union
  import torch
  import torch.nn
  import torchvision.models
  from .utils import FeatureExtractorBackbone
label_desc: Use image node_id 3 for calling the SLDAResNetModel obj's underlying member method code with example usage: obj.forward(x) and returns: feat