def NASNetMobile(
    input_shape=None,
    include_top=True,
    weights="imagenet",
    input_tensor=None,
    pooling=None,
    classes=1000,
    classifier_activation="softmax",
    name="nasnet_mobile",
):
    """Instantiates a Mobile NASNet model in ImageNet mode.

    Reference:
    - [Learning Transferable Architectures for Scalable Image Recognition](
        https://arxiv.org/abs/1707.07012) (CVPR 2018)

    Optionally loads weights pre-trained on ImageNet.
    Note that the data format convention used by the model is
    the one specified in your Keras config at `~/.keras/keras.json`.

    Note: each Keras Application expects a specific kind of input preprocessing.
    For NASNet, call `keras.applications.nasnet.preprocess_input` on your
    inputs before passing them to the model.

    Args:
        input_shape: Optional shape tuple, only to be specified
            if `include_top` is False (otherwise the input shape
            has to be `(224, 224, 3)` for NASNetMobile
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 32.
            E.g. `(224, 224, 3)` would be one valid value.
        include_top: Whether to include the fully-connected
            layer at the top of the network.
        weights: `None` (random initialization) or
            `imagenet` (ImageNet weights). For loading `imagenet` weights,
            `input_shape` should be (224, 224, 3)
        input_tensor: Optional Keras tensor (i.e. output of
            `layers.Input()`)
            to use as image input for the model.
        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 layer.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional layer, 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"`.
        name: The name of the model (string).

    Returns:
        A Keras model instance.
    """
    if backend.backend() == "torch":
        raise ValueError(
            "NASNetMobile is not available with the torch backend "
            "at this time due to an outstanding bug. "
            "If interested, please open a PR."
        )
    if not include_top and input_shape is None:
        input_shape = (224, 224, 3)
    return NASNet(
        input_shape,
        penultimate_filters=1056,
        num_blocks=4,
        stem_block_filters=32,
        skip_reduction=False,
        filter_multiplier=2,
        include_top=include_top,
        weights=weights,
        input_tensor=input_tensor,
        pooling=pooling,
        classes=classes,
        default_size=224,
        classifier_activation=classifier_activation,
        name=name,
    )
Instantiates a Mobile NASNet model in ImageNet mode.

    Reference:
    - [Learning Transferable Architectures for Scalable Image Recognition](
        https://arxiv.org/abs/1707.07012) (CVPR 2018)

    Optionally loads weights pre-trained on ImageNet.
    Note that the data format convention used by the model is
    the one specified in your Keras config at `~/.keras/keras.json`.

    Note: each Keras Application expects a specific kind of input preprocessing.
    For NASNet, call `keras.applications.nasnet.preprocess_input` on your
    inputs before passing them to the model.

    Args:
        input_shape: Optional shape tuple, only to be specified
            if `include_top` is False (otherwise the input shape
            has to be `(224, 224, 3)` for NASNetMobile
            It should have exactly 3 inputs channels,
            and width and height should be no smaller than 32.
            E.g. `(224, 224, 3)` would be one valid value.
        include_top: Whether to include the fully-connected
            layer at the top of the network.
        weights: `None` (random initialization) or
            `imagenet` (ImageNet weights). For loading `imagenet` weights,
            `input_shape` should be (224, 224, 3)
        input_tensor: Optional Keras tensor (i.e. output of
            `layers.Input()`)
            to use as image input for the model.
        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 layer.
            - `avg` means that global average pooling
                will be applied to the output of the
                last convolutional layer, 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"`.
        name: The name of the model (string).

    Returns:
        A Keras model instance.
    
keras/src/applications/nasnet.py