def METHOD_NAME(inputs,
               num_classes=1000,
               is_training=True,
               dropout_keep_prob=0.5,
               spatial_squeeze=True,
               scope='alexnet_v2'):
  """AlexNet version 2.
  Described in: http://arxiv.org/pdf/1404.5997v2.pdf
  Parameters from:
  github.com/akrizhevsky/cuda-convnet2/blob/master/layers/
  layers-imagenet-1gpu.cfg
  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 224x224. To use in fully
        convolutional mode, set spatial_squeeze to false.
        The LRN layers have been removed and change the initializers from
        random_normal_initializer to xavier_initializer.
  Args:
    inputs: a tensor of size [batch_size, height, width, channels].
    num_classes: number of predicted classes.
    is_training: whether or not the model is being trained.
    dropout_keep_prob: the probability that activations are kept in the dropout
      layers during training.
    spatial_squeeze: whether or not should squeeze the spatial dimensions of the
      outputs. Useful to remove unnecessary dimensions for classification.
    scope: Optional scope for the variables.
  Returns:
    the last op containing the log predictions and end_points dict.
  """
  with variable_scope.variable_scope(scope, 'alexnet_v2', [inputs]) as sc:
    end_points_collection = sc.original_name_scope + '_end_points'
    # Collect outputs for conv2d, fully_connected and max_pool2d.
    with arg_scope(
        [layers.conv2d, layers_lib.fully_connected, layers_lib.max_pool2d],
        outputs_collections=[end_points_collection]):
      net = layers.conv2d(
          inputs, 64, [11, 11], 4, padding='VALID', scope='conv1')
      net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool1')
      net = layers.conv2d(net, 192, [5, 5], scope='conv2')
      net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool2')
      net = layers.conv2d(net, 384, [3, 3], scope='conv3')
      net = layers.conv2d(net, 384, [3, 3], scope='conv4')
      net = layers.conv2d(net, 256, [3, 3], scope='conv5')
      net = layers_lib.max_pool2d(net, [3, 3], 2, scope='pool5')
      # Use conv2d instead of fully_connected layers.
      with arg_scope(
          [layers.conv2d],
          weights_initializer=trunc_normal(0.005),
          biases_initializer=init_ops.constant_initializer(0.1)):
        net = layers.conv2d(net, 4096, [5, 5], padding='VALID', scope='fc6')
        net = layers_lib.dropout(
            net, dropout_keep_prob, is_training=is_training, scope='dropout6')
        net = layers.conv2d(net, 4096, [1, 1], scope='fc7')
        net = layers_lib.dropout(
            net, dropout_keep_prob, is_training=is_training, scope='dropout7')
        net = layers.conv2d(
            net,
            num_classes, [1, 1],
            activation_fn=None,
            normalizer_fn=None,
            biases_initializer=init_ops.zeros_initializer(),
            scope='fc8')
      # Convert end_points_collection into a end_point dict.
      end_points = utils.convert_collection_to_dict(end_points_collection)
      if spatial_squeeze:
        net = array_ops.squeeze(net, [1, 2], name='fc8/squeezed')
        end_points[sc.name + '/fc8'] = net
      return net, end_points