code: def EfficientNet(
      width_coefficient,
      depth_coefficient,
      default_size,
      dropout_rate=0.2,
      drop_connect_rate=0.2,
      depth_divisor=8,
      activation="swish",
      blocks_args="default",
      model_name="efficientnet",
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  ):
      """Instantiates the EfficientNet architecture.
  
      Args:
        width_coefficient: float, scaling coefficient for network width.
        depth_coefficient: float, scaling coefficient for network depth.
        default_size: integer, default input image size.
        dropout_rate: float, dropout rate before final classifier layer.
        drop_connect_rate: float, dropout rate at skip connections.
        depth_divisor: integer, a unit of network width.
        activation: activation function.
        blocks_args: list of dicts, parameters to construct block modules.
        model_name: string, model name.
        include_top: whether to include the fully-connected
            layer 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.
            It should have exactly 3 inputs channels.
        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.
  
      Returns:
          A model instance.
      """
      if blocks_args == "default":
          blocks_args = DEFAULT_BLOCKS_ARGS
  
      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="imagenet"` with `include_top`'
              " as true, `classes` should be 1000"
          )
  
      # Determine proper input shape
      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 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
  
      bn_axis = (
          3 if backend.image_data_format() == "channels_last" else 1
      )
  
      def round_filters(filters, divisor=depth_divisor):
          """Round number of filters based on depth multiplier."""
          filters *= width_coefficient
          new_filters = max(
              divisor, int(filters + divisor / 2) // divisor * divisor
          )
          # Make sure that round down does not go down by more than 10%.
          if new_filters < 0.9 * filters:
              new_filters += divisor
          return int(new_filters)
  
      def round_repeats(repeats):
          """Round number of repeats based on depth multiplier."""
          return int(math.ceil(depth_coefficient * repeats))
  
      # Build stem
      x = img_input
      x = layers.Rescaling(1.0 / 255.0)(x)
      x = layers.Normalization(axis=bn_axis)(x)
      if weights == "imagenet":
          # Note that the normaliztion layer uses square value of STDDEV as the
          # variance for the layer: result = (input - mean) / sqrt(var)
          # However, the original implemenetation uses (input - mean) / var to
          # normalize the input, we need to divide another sqrt(var) to match the
          # original implementation.
          # See https://github.com/tensorflow/tensorflow/issues/49930 for more
          # details
          x = layers.Rescaling(
              [
                  1.0 / math.sqrt(stddev)
                  for stddev in IMAGENET_STDDEV_RGB
              ]
          )(x)
  
      x = layers.ZeroPadding2D(
          padding=imagenet_utils.correct_pad(x, 3), name="stem_conv_pad"
      )(x)
      x = layers.Conv2D(
          round_filters(32),
          3,
          strides=2,
          padding="valid",
          use_bias=False,
          kernel_initializer=CONV_KERNEL_INITIALIZER,
          name="stem_conv",
      )(x)
      x = layers.BatchNormalization(axis=bn_axis, name="stem_bn")(x)
      x = layers.Activation(activation, name="stem_activation")(x)
  
      # Build blocks
      blocks_args = copy.deepcopy(blocks_args)
  
      b = 0
      blocks = float(
          sum(round_repeats(args["repeats"]) for args in blocks_args)
      )
      for i, args in enumerate(blocks_args):
          assert args["repeats"] > 0
          # Update block input and output filters based on depth multiplier.
          args["filters_in"] = round_filters(args["filters_in"])
          args["filters_out"] = round_filters(args["filters_out"])
  
          for j in range(round_repeats(args.pop("repeats"))):
              # The first block needs to take care of stride and filter size
              # increase.
              if j > 0:
                  args["strides"] = 1
                  args["filters_in"] = args["filters_out"]
              x = block(
                  x,
                  activation,
                  drop_connect_rate * b / blocks,
                  name=f"block{i + 1}{chr(j + 97)}_",
                  **args,
              )
              b += 1
  
      # Build top
      x = layers.Conv2D(
          round_filters(1280),
          1,
          padding="same",
          use_bias=False,
          kernel_initializer=CONV_KERNEL_INITIALIZER,
          name="top_conv",
      )(x)
      x = layers.BatchNormalization(axis=bn_axis, name="top_bn")(x)
      x = layers.Activation(activation, name="top_activation")(x)
      if include_top:
          x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
          if dropout_rate > 0:
              x = layers.Dropout(dropout_rate, name="top_dropout")(x)
          imagenet_utils.validate_activation(
              classifier_activation, weights
          )
          x = layers.Dense(
              classes,
              activation=classifier_activation,
              kernel_initializer=DENSE_KERNEL_INITIALIZER,
              name="predictions",
          )(x)
      else:
          if pooling == "avg":
              x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
          elif pooling == "max":
              x = layers.GlobalMaxPooling2D(name="max_pool")(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
  
      # Create model.
      model = Functional(inputs, x, name=model_name)
  
      # Load weights.
      if weights == "imagenet":
          if include_top:
              file_suffix = ".h5"
              file_hash = WEIGHTS_HASHES[model_name[-2:]][0]
          else:
              file_suffix = "_notop.h5"
              file_hash = WEIGHTS_HASHES[model_name[-2:]][1]
          file_name = model_name + file_suffix
          weights_path = file_utils.get_file(
              file_name,
              BASE_WEIGHTS_PATH + file_name,
              cache_subdir="models",
              file_hash=file_hash,
          )
          model.load_weights(weights_path)
      elif weights is not None:
          model.load_weights(weights)
      return model
  
imports: import copy
  import math
  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: EfficientNet(width_coefficient, depth_coefficient, default_size, dropout_rate, drop_connect_rate, depth_divisor, activation, blocks_args, model_name, include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation) and returns: model, int, int