code: def EfficientNetV2(
      width_coefficient,
      depth_coefficient,
      default_size,
      dropout_rate=0.2,
      drop_connect_rate=0.2,
      depth_divisor=8,
      min_depth=8,
      bn_momentum=0.9,
      activation="swish",
      blocks_args="default",
      model_name="efficientnetv2",
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
      include_preprocessing=True,
  ):
      """Instantiates the EfficientNetV2 architecture using given scaling
      coefficients.
  
      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.
          min_depth: integer, minimum number of filters.
          bn_momentum: float. Momentum parameter for Batch Normalization layers.
          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()`) or
              numpy array 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 string 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.
          include_preprocessing: Boolean, whether to include the preprocessing
              layer (`Rescaling`) at the bottom of the network.
              Defaults to `True`.
  
      Returns:
          A model instance.
      """
  
      if blocks_args == "default":
          blocks_args = DEFAULT_BLOCKS_ARGS[model_name]
  
      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."
              f"Received: weights={weights}"
          )
  
      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
      )
  
      x = img_input
  
      if include_preprocessing:
          # Apply original V1 preprocessing for Bx variants
          # if number of channels allows it
          num_channels = input_shape[bn_axis - 1]
          if (
              model_name.split("-")[-1].startswith("b")
              and num_channels == 3
          ):
              x = layers.Rescaling(scale=1.0 / 255)(x)
              x = layers.Normalization(
                  mean=[0.485, 0.456, 0.406],
                  variance=[0.229**2, 0.224**2, 0.225**2],
                  axis=bn_axis,
              )(x)
          else:
              x = layers.Rescaling(scale=1.0 / 128.0, offset=-1)(x)
  
      # Build stem
      stem_filters = round_filters(
          filters=blocks_args[0]["input_filters"],
          width_coefficient=width_coefficient,
          min_depth=min_depth,
          depth_divisor=depth_divisor,
      )
      x = layers.Conv2D(
          filters=stem_filters,
          kernel_size=3,
          strides=2,
          kernel_initializer=CONV_KERNEL_INITIALIZER,
          padding="same",
          use_bias=False,
          name="stem_conv",
      )(x)
      x = layers.BatchNormalization(
          axis=bn_axis,
          momentum=bn_momentum,
          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(args["num_repeat"] for args in blocks_args))
  
      for i, args in enumerate(blocks_args):
          assert args["num_repeat"] > 0
  
          # Update block input and output filters based on depth multiplier.
          args["input_filters"] = round_filters(
              filters=args["input_filters"],
              width_coefficient=width_coefficient,
              min_depth=min_depth,
              depth_divisor=depth_divisor,
          )
          args["output_filters"] = round_filters(
              filters=args["output_filters"],
              width_coefficient=width_coefficient,
              min_depth=min_depth,
              depth_divisor=depth_divisor,
          )
  
          # Determine which conv type to use:
          block = {0: MBConvBlock, 1: FusedMBConvBlock}[
              args.pop("conv_type")
          ]
          repeats = round_repeats(
              repeats=args.pop("num_repeat"),
              depth_coefficient=depth_coefficient,
          )
          for j in range(repeats):
              # The first block needs to take care of stride and filter size
              # increase.
              if j > 0:
                  args["strides"] = 1
                  args["input_filters"] = args["output_filters"]
  
              x = block(
                  activation=activation,
                  bn_momentum=bn_momentum,
                  survival_probability=drop_connect_rate * b / blocks,
                  name=f"block{i + 1}{chr(j + 97)}_",
                  **args,
              )(x)
              b += 1
  
      # Build top
      top_filters = round_filters(
          filters=1280,
          width_coefficient=width_coefficient,
          min_depth=min_depth,
          depth_divisor=depth_divisor,
      )
      x = layers.Conv2D(
          filters=top_filters,
          kernel_size=1,
          strides=1,
          kernel_initializer=CONV_KERNEL_INITIALIZER,
          padding="same",
          data_format=backend.image_data_format(),
          use_bias=False,
          name="top_conv",
      )(x)
      x = layers.BatchNormalization(
          axis=bn_axis,
          momentum=bn_momentum,
          name="top_bn",
      )(x)
      x = layers.Activation(
          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,
              bias_initializer=initializers.Constant(0.0),
              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 initializers
  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 5 for calling a global function with example usage: EfficientNetV2(width_coefficient, depth_coefficient, default_size, dropout_rate, drop_connect_rate, depth_divisor, min_depth, bn_momentum, activation, blocks_args, model_name, include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation, include_preprocessing) and returns: model