code: def NASNet(
      input_shape=None,
      penultimate_filters=4032,
      num_blocks=6,
      stem_block_filters=96,
      skip_reduction=True,
      filter_multiplier=2,
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      pooling=None,
      classes=1000,
      default_size=None,
      classifier_activation="softmax",
  ):
      """Instantiates a NASNet model.
  
      Reference:
      - [Learning Transferable Architectures for Scalable Image Recognition](
          https://arxiv.org/abs/1707.07012) (CVPR 2018)
  
      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/).
  
      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.
      `nasnet.preprocess_input` will scale input pixels between -1 and 1.
  
      Args:
          input_shape: Optional shape tuple, the input shape
              is by default `(331, 331, 3)` for NASNetLarge and
              `(224, 224, 3)` for NASNetMobile.
              It should have exactly 3 input channels,
              and width and height should be no smaller than 32.
              E.g. `(224, 224, 3)` would be one valid value.
          penultimate_filters: Number of filters in the penultimate layer.
              NASNet models use the notation `NASNet (N @ P)`, where:
                  -   N is the number of blocks
                  -   P is the number of penultimate filters
          num_blocks: Number of repeated blocks of the NASNet model.
              NASNet models use the notation `NASNet (N @ P)`, where:
                  -   N is the number of blocks
                  -   P is the number of penultimate filters
          stem_block_filters: Number of filters in the initial stem block
          skip_reduction: Whether to skip the reduction step at the tail
              end of the network.
          filter_multiplier: Controls the width of the network.
              - If `filter_multiplier` < 1.0, proportionally decreases the number
                  of filters in each layer.
              - If `filter_multiplier` > 1.0, proportionally increases the number
                  of filters in each layer.
              - If `filter_multiplier` = 1, default number of filters from the
                  paper are used at each layer.
          include_top: Whether to include the fully-connected
              layer at the top of the network.
          weights: `None` (random initialization) or
              `imagenet` (ImageNet weights)
          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 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.
          default_size: Specifies the default image size of the model
          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 model instance.
      """
      if backend.image_data_format() == "channels_first":
          raise ValueError(
              "NASNet does not support the `channels_first` image data "
              "format. Switch to `channels_last` by editing your local "
              "config file at ~/.keras/keras.json"
          )
      if not (
          weights in {"imagenet", None} or file_utils.exists(weights)
      ):
          raise ValueError(
              "The `weights` argument should be either "
              "`None` (random initialization), `imagenet` "
              "(pre-training on ImageNet), "
              "or the path to the weights file to be loaded."
          )
  
      if weights == "imagenet" and include_top and classes != 1000:
          raise ValueError(
              'If using `weights` as `"imagenet"` with `include_top` '
              "as true, `classes` should be 1000"
          )
  
      if (
          isinstance(input_shape, tuple)
          and None in input_shape
          and weights == "imagenet"
      ):
          raise ValueError(
              "When specifying the input shape of a NASNet"
              " and loading `ImageNet` weights, "
              "the input_shape argument must be static "
              "(no None entries). Got: `input_shape="
              + str(input_shape)
              + "`."
          )
  
      if default_size is None:
          default_size = 331
  
      # Determine proper input shape and default size.
      input_shape = imagenet_utils.obtain_input_shape(
          input_shape,
          default_size=default_size,
          min_size=32,
          data_format=backend.image_data_format(),
          require_flatten=include_top,
          weights=weights,
      )
  
      if backend.image_data_format() != "channels_last":
          warnings.warn(
              "The NASNet family of models is only available "
              'for the input data format "channels_last" '
              "(width, height, channels). "
              "However your settings specify the default "
              'data format "channels_first" (channels, width, height).'
              ' You should set `image_data_format="channels_last"` '
              "in your Keras config located at ~/.keras/keras.json. "
              "The model being returned right now will expect inputs "
              'to follow the "channels_last" data format.',
              stacklevel=2,
          )
  
      if input_tensor is None:
          img_input = layers.Input(shape=input_shape)
      else:
          if not backend.is_keras_tensor(input_tensor):
              img_input = layers.Input(
                  tensor=input_tensor, shape=input_shape
              )
          else:
              img_input = input_tensor
  
      if penultimate_filters % (24 * (filter_multiplier**2)) != 0:
          raise ValueError(
              "For NASNet-A models, the `penultimate_filters` must be a multiple "
              "of 24 * (`filter_multiplier` ** 2). Current value: %d"
              % penultimate_filters
          )
  
      channel_dim = (
          1 if backend.image_data_format() == "channels_first" else -1
      )
      filters = penultimate_filters // 24
  
      x = layers.Conv2D(
          stem_block_filters,
          (3, 3),
          strides=(2, 2),
          padding="valid",
          use_bias=False,
          name="stem_conv1",
          kernel_initializer="he_normal",
      )(img_input)
  
      x = layers.BatchNormalization(
          axis=channel_dim,
          momentum=0.9997,
          epsilon=1e-3,
          name="stem_bn1",
      )(x)
  
      p = None
      x, p = _reduction_a_cell(
          x, p, filters // (filter_multiplier**2), block_id="stem_1"
      )
      x, p = _reduction_a_cell(
          x, p, filters // filter_multiplier, block_id="stem_2"
      )
  
      for i in range(num_blocks):
          x, p = _normal_a_cell(x, p, filters, block_id="%d" % (i))
  
      x, p0 = _reduction_a_cell(
          x,
          p,
          filters * filter_multiplier,
          block_id="reduce_%d" % (num_blocks),
      )
  
      p = p0 if not skip_reduction else p
  
      for i in range(num_blocks):
          x, p = _normal_a_cell(
              x,
              p,
              filters * filter_multiplier,
              block_id="%d" % (num_blocks + i + 1),
          )
  
      x, p0 = _reduction_a_cell(
          x,
          p,
          filters * filter_multiplier**2,
          block_id="reduce_%d" % (2 * num_blocks),
      )
  
      p = p0 if not skip_reduction else p
  
      for i in range(num_blocks):
          x, p = _normal_a_cell(
              x,
              p,
              filters * filter_multiplier**2,
              block_id="%d" % (2 * num_blocks + i + 1),
          )
  
      x = layers.Activation("relu")(x)
  
      if include_top:
          x = layers.GlobalAveragePooling2D()(x)
          imagenet_utils.validate_activation(
              classifier_activation, weights
          )
          x = layers.Dense(
              classes,
              activation=classifier_activation,
              name="predictions",
          )(x)
      else:
          if pooling == "avg":
              x = layers.GlobalAveragePooling2D()(x)
          elif pooling == "max":
              x = layers.GlobalMaxPooling2D()(x)
  
      # Ensure that the model takes into account
      # any potential predecessors of `input_tensor`.
      if input_tensor is not None:
          inputs = operation_utils.get_source_inputs(input_tensor)
      else:
          inputs = img_input
  
      model = Functional(inputs, x, name="NASNet")
  
      # Load weights.
      if weights == "imagenet":
          if default_size == 224:  # mobile version
              if include_top:
                  weights_path = file_utils.get_file(
                      "nasnet_mobile.h5",
                      NASNET_MOBILE_WEIGHT_PATH,
                      cache_subdir="models",
                      file_hash="020fb642bf7360b370c678b08e0adf61",
                  )
              else:
                  weights_path = file_utils.get_file(
                      "nasnet_mobile_no_top.h5",
                      NASNET_MOBILE_WEIGHT_PATH_NO_TOP,
                      cache_subdir="models",
                      file_hash="1ed92395b5b598bdda52abe5c0dbfd63",
                  )
              model.load_weights(weights_path)
          elif default_size == 331:  # large version
              if include_top:
                  weights_path = file_utils.get_file(
                      "nasnet_large.h5",
                      NASNET_LARGE_WEIGHT_PATH,
                      cache_subdir="models",
                      file_hash="11577c9a518f0070763c2b964a382f17",
                  )
              else:
                  weights_path = file_utils.get_file(
                      "nasnet_large_no_top.h5",
                      NASNET_LARGE_WEIGHT_PATH_NO_TOP,
                      cache_subdir="models",
                      file_hash="d81d89dc07e6e56530c4e77faddd61b5",
                  )
              model.load_weights(weights_path)
          else:
              raise ValueError(
                  "ImageNet weights can only be loaded with NASNetLarge"
                  " or NASNetMobile"
              )
      elif weights is not None:
          model.load_weights(weights)
  
      return model
  
file_path: keras/keras/applications/nasnet.py
imports: import warnings
  from keras import backend
  from keras import layers
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.models import Functional
  from keras.ops import operation_utils
  from keras.utils import file_utils
label_desc: Use image node_id 1 for calling a global function with example usage: NASNet(input_shape, penultimate_filters, num_blocks, stem_block_filters, skip_reduction, filter_multiplier, include_top, weights, input_tensor, pooling, classes, default_size, classifier_activation) and returns: model