def METHOD_NAME(inputs,
             num_classes=1000,
             is_training=True,
             dropout_keep_prob=0.5,
             spatial_squeeze=True,
             scope='overfeat'):
  """Contains the model definition for the OverFeat network.
  The definition for the network was obtained from:
    OverFeat: Integrated Recognition, Localization and Detection using
    Convolutional Networks
    Pierre Sermanet, David Eigen, Xiang Zhang, Michael Mathieu, Rob Fergus and
    Yann LeCun, 2014
    http://arxiv.org/abs/1312.6229
  Note: All the fully_connected layers have been transformed to conv2d layers.
        To use in classification mode, resize input to 231x231. To use in fully
        convolutional mode, set spatial_squeeze to false.
  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, 'overfeat', [inputs]) as sc:
    end_points_collection = sc.name + '_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, [2, 2], scope='pool1')
      net = layers.conv2d(net, 256, [5, 5], padding='VALID', scope='conv2')
      net = layers_lib.max_pool2d(net, [2, 2], scope='pool2')
      net = layers.conv2d(net, 512, [3, 3], scope='conv3')
      net = layers.conv2d(net, 1024, [3, 3], scope='conv4')
      net = layers.conv2d(net, 1024, [3, 3], scope='conv5')
      net = layers_lib.max_pool2d(net, [2, 2], scope='pool5')
      with arg_scope(
          [layers.conv2d],
          weights_initializer=trunc_normal(0.005),
          biases_initializer=init_ops.constant_initializer(0.1)):
        # Use conv2d instead of fully_connected layers.
        net = layers.conv2d(net, 3072, [6, 6], 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