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.
code: def pool_feat(features):
      feat_size = features.shape[-1]
      num_channels = features.shape[1]
      features2 = features.permute(
          0, 2, 3, 1
      )  # 1 x feat_size x feat_size x
      # num_channels
      features3 = torch.reshape(
          features2,
          (features.shape[0], feat_size * feat_size, num_channels),
      )
      feat = features3.mean(1)  # mb x num_channels
      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 2 for calling the SLDAResNetModel obj's underlying member method code with example usage: obj.pool_feat(features) and returns: feat