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Reference:
  - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
      https://arxiv.org/abs/1409.1556) (ICLR 2015)
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S )<a%  Instantiates the VGG19 architecture.

    Reference:
    - [Very Deep Convolutional Networks for Large-Scale Image Recognition](
        https://arxiv.org/abs/1409.1556) (ICLR 2015)

    For image classification use cases, see
    [this page for detailed examples](
      https://keras.io/api/applications/#usage-examples-for-image-classification-models).

    For transfer learning use cases, make sure to read the
    [guide to transfer learning & fine-tuning](
      https://keras.io/guides/transfer_learning/).

    The default input size for this model is 224x224.

    Note: each Keras Application expects a specific kind of input preprocessing.
    For VGG19, call `tf.keras.applications.vgg19.preprocess_input` on your
    inputs before passing them to the model.
    `vgg19.preprocess_input` will convert the input images from RGB to BGR,
    then will zero-center each color channel with respect to the ImageNet
    dataset, without scaling.

    Args:
      include_top: whether to include the 3 fully-connected
        layers at the top of the network.
      weights: one of `None` (random initialization),
          'imagenet' (pre-training on ImageNet),
          or the path to the weights file to be loaded.
      input_tensor: optional Keras tensor
        (i.e. output of `layers.Input()`)
        to use as image input for the model.
      input_shape: optional shape tuple, only to be specified
        if `include_top` is False (otherwise the input shape
        has to be `(224, 224, 3)`
        (with `channels_last` data format)
        or `(3, 224, 224)` (with `channels_first` data format).
        It should have exactly 3 inputs channels,
        and width and height should be no smaller than 32.
        E.g. `(200, 200, 3)` would be one valid value.
      pooling: Optional pooling mode for feature extraction
        when `include_top` is `False`.
        - `None` means that the output of the model will be
            the 4D tensor output of the
            last convolutional block.
        - `avg` means that global average pooling
            will be applied to the output of the
            last convolutional block, and thus
            the output of the model will be a 2D tensor.
        - `max` means that global max pooling will
            be applied.
      classes: optional number of classes to classify images
        into, only to be specified if `include_top` is True, and
        if no `weights` argument is specified.
      classifier_activation: A `str` or callable. The activation function to use
        on the "top" layer. Ignored unless `include_top=True`. Set
        `classifier_activation=None` to return the logits of the "top" layer.
        When loading pretrained weights, `classifier_activation` can only
        be `None` or `"softmax"`.

    Returns:
      A `keras.Model` instance.
    >   r	   NzThe `weights` argument should be either `None` (random initialization), `imagenet` (pre-training on ImageNet), or the path to the weights file to be loaded.  Received: `weights=z.`r	   r
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r$   z)keras.applications.vgg19.preprocess_inputc                 C   s   t j| |ddS )NZcaffe)r   mode)r   preprocess_input)r!   r   r"   r"   r#   r&     s   r&   z+keras.applications.vgg19.decode_predictions   c                 C   s   t j| |dS )N)top)r   decode_predictions)Zpredsr(   r"   r"   r#   r)     s   r)    )r%   reterror)Tr	   NNNr
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