class_docstr: MobileNet v1 implementation. This model
  can be instantiated from a pretrained network.
code: def __init__(self, pretrained=True, latent_layer_num=20):
      super().__init__()
  
      model = mobilenet_w1(pretrained=pretrained)
      model.features.final_pool = nn.AvgPool2d(4)
  
      all_layers: List[nn.Module] = []
      remove_sequential(model, all_layers)
      all_layers = remove_DwsConvBlock(all_layers)
  
      lat_list = []
      end_list = []
  
      for i, layer in enumerate(all_layers[:-1]):
          if i <= latent_layer_num:
              lat_list.append(layer)
          else:
              end_list.append(layer)
  
      self.lat_features = nn.Sequential(*lat_list)
      self.end_features = nn.Sequential(*end_list)
  
      self.output = nn.Linear(1024, 50, bias=False)
  
file_path: avalanche/avalanche/models/mobilenetv1.py
imports: from typing import List
  import torch.nn
  import torch
  from pytorchcv.models.mobilenet import mobilenet_w1
label_desc: Use image node_id 1 to create a new MobilenetV1 object from inherited base classes: nn with example: obj = MobilenetV1(pretrained, latent_layer_num)