def __call__(self, inputs, is_training=False):
    'Returns the ResNet model for a given size and number of output classes.\n\n    Args:\n      inputs: a `Tesnor` with shape [batch_size, height, width, 3] representing\n        a batch of images.\n      is_training: `bool` if True, the model is in training mode.\n\n    Returns:\n      a `dict` containing `int` keys for continuous feature levels [2, 3, 4, 5].\n      The values are corresponding feature hierarchy in ResNet with shape\n      [batch_size, height_l, width_l, num_filters].\n    '
    with tf.variable_scope(('resnet%s' % self._resnet_depth)):
        return self._resnet_fn(inputs, is_training)
Returns the ResNet model for a given size and number of output classes.

Args:
  inputs: a `Tesnor` with shape [batch_size, height, width, 3] representing
    a batch of images.
  is_training: `bool` if True, the model is in training mode.

Returns:
  a `dict` containing `int` keys for continuous feature levels [2, 3, 4, 5].
  The values are corresponding feature hierarchy in ResNet with shape
  [batch_size, height_l, width_l, num_filters].
def __call__(self, inputs, is_training=False):
    'Returns the ResNet model for a given size and number of output classes.\n\n    Args:\n      inputs: a `Tesnor` with shape [batch_size, height, width, 3] representing\n        a batch of images.\n      is_training: `bool` if True, the model is in training mode.\n\n    Returns:\n      a `dict` containing `int` keys for continuous feature levels [2, 3, 4, 5].\n      The values are corresponding feature hierarchy in ResNet with shape\n      [batch_size, height_l, width_l, num_filters].\n    '
    with tf.variable_scope(('resnet%s' % self._resnet_depth)):
        return self._resnet_fn(inputs, is_training)