class_docstr: A pre-trained CNN model provided by VGG team.
  
  You can use ``VGG16Layers`` or ``VGG19Layers`` for concrete
  implementations. During initialization, this chain model
  automatically downloads the pre-trained caffemodel, convert to
  another chainer model, stores it on your local directory,
  and initializes all the parameters with it.
  This model would be useful when you want to extract a semantic
  feature vector from a given image, or fine-tune the model
  on a different dataset.
  Note that these pre-trained models are released under Creative Commons
  Attribution License.
  
  If you want to manually convert the pre-trained caffemodel to a chainer
  model that can be specified in the constructor,
  please use ``convert_caffemodel_to_npz`` classmethod instead.
  
  See: K. Simonyan and A. Zisserman, `Very Deep Convolutional Networks
  for Large-Scale Image Recognition <https://arxiv.org/abs/1409.1556>`_
  
  Args:
      pretrained_model (str): the destination of the pre-trained
          chainer model serialized as a ``.npz`` file.
          If this argument is specified as ``auto``,
          it automatically downloads the caffemodel from the internet.
          Note that in this case the converted chainer model is stored
          on ``$CHAINER_DATASET_ROOT/pfnet/chainer/models`` directory,
          where ``$CHAINER_DATASET_ROOT`` is set as
          ``$HOME/.chainer/dataset`` unless you specify another value
          as a environment variable. The converted chainer model is
          automatically used from the second time.
          If the argument is specified as ``None``, all the parameters
          are not initialized by the pre-trained model, but the default
          initializer used in the original paper, i.e.,
          ``chainer.initializers.Normal(scale=0.01)``.
      n_layers (int): The number of layers of this model. It should be
          either 16 or 19.
  
  Attributes:
      available_layers (list of str): The list of available layer names
          used by ``forward`` and ``extract`` methods.
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code: def functions(self):
      # This class will not be used directly.
      raise NotImplementedError
  
imports: import collections
  import os
  import sys
  import numpy
  import chainer
  from chainer.dataset.convert import concat_examples
  from chainer.dataset import download
  from chainer import function
  from chainer.functions.activation.relu import relu
  from chainer.functions.activation.softmax import softmax
  from chainer.functions.array.reshape import reshape
  from chainer.functions.math.sum import sum
  from chainer.functions.noise.dropout import dropout
  from chainer.functions.pooling.max_pooling_nd import max_pooling_2d
  from chainer.initializers import constant
  from chainer.initializers import normal
  from chainer import link
  from chainer.links.connection.convolution_2d import Convolution2D
  from chainer.links.connection.linear import Linear
  from chainer.serializers import npz
  from chainer.utils import argument
  from chainer.utils import imgproc
  from chainer.variable import Variable
label_desc: Use image node_id 2 for calling the VGGLayers obj's underlying member method code with example usage: obj.functions() without return types