class_docstr: VGG model from the `"Very Deep Convolutional Networks for Large-Scale Image Recognition"
  <https://arxiv.org/abs/1409.1556>`_ paper.
  
  Parameters
  ----------
  layers : list of int
      Numbers of layers in each feature block.
  filters : list of int
      Numbers of filters in each feature block. List length should match the layers.
  classes : int, default 1000
      Number of classification classes.
  batch_norm : bool, default False
      Use batch normalization.
class_docstr_tok: ["VGG","model","from","the","`","``","Very","Deep","Convolutional","Networks","for","Large-Scale","Image","Recognition","''","<","https",":","\/\/arxiv.org\/abs\/1409.1556",">","`","_","paper",".","Parameters","--","--","--","--","--","layers",":","list","of","int","Numbers","of","layers","in","each","feature","block",".","filters",":","list","of","int","Numbers","of","filters","in","each","feature","block",".","List","length","should","match","the","layers",".","classes",":","int",",","default","1000","Number","of","classification","classes",".","batch_norm",":","bool",",","default","False","Use","batch","normalization","."]
code: def _make_features(self, layers, filters, batch_norm):
      featurizer = nn.HybridSequential()
      for i, num in enumerate(layers):
          for _ in range(num):
              featurizer.add(
                  nn.Conv2D(
                      filters[i],
                      kernel_size=3,
                      padding=1,
                      weight_initializer=Xavier(
                          rnd_type="gaussian",
                          factor_type="out",
                          magnitude=2,
                      ),
                      bias_initializer="zeros",
                  )
              )
              if batch_norm:
                  featurizer.add(nn.BatchNorm())
              featurizer.add(nn.Activation("relu"))
          featurizer.add(nn.MaxPool2D(strides=2))
      return featurizer
  
imports: import os
  from ....device import cpu
  from ....initializer import Xavier
  from ...block import HybridBlock
  from ...None import nn
  from ....None import base
  from ....util import use_np, wrap_ctx_to_device_func
label_desc: Use image node_id 2 for calling the VGG obj's underlying member method code with example usage: obj._make_features(layers, filters, batch_norm) and returns: featurizer