code: def InceptionResNetV2(
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      classifier_activation="softmax",
  ):
      """Instantiates the Inception-ResNet v2 architecture.
  
      Reference:
      - [Inception-v4, Inception-ResNet and the Impact of
         Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
        (AAAI 2017)
  
      This function returns a Keras image classification model,
      optionally loaded with weights pre-trained on ImageNet.
  
      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 InceptionResNetV2, call
      `keras.applications.inception_resnet_v2.preprocess_input`
      on your inputs before passing them to the model.
      `inception_resnet_v2.preprocess_input`
      will scale input pixels between -1 and 1.
  
      Args:
          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` (otherwise the input shape
              has to be `(299, 299, 3)`
              (with `'channels_last'` data format)
              or `(3, 299, 299)` (with `'channels_first'` data format).
              It should have exactly 3 inputs channels,
              and width and height should be no smaller than 75.
              E.g. `(150, 150, 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 model instance.
      """
      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=True`, '
              "`classes` should be 1000. "
              f"Received classes={classes}"
          )
  
      # Determine proper input shape
      input_shape = imagenet_utils.obtain_input_shape(
          input_shape,
          default_size=299,
          min_size=75,
          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
  
      # Stem block: 35 x 35 x 192
      x = conv2d_bn(img_input, 32, 3, strides=2, padding="valid")
      x = conv2d_bn(x, 32, 3, padding="valid")
      x = conv2d_bn(x, 64, 3)
      x = layers.MaxPooling2D(3, strides=2)(x)
      x = conv2d_bn(x, 80, 1, padding="valid")
      x = conv2d_bn(x, 192, 3, padding="valid")
      x = layers.MaxPooling2D(3, strides=2)(x)
  
      # Mixed 5b (Inception-A block): 35 x 35 x 320
      branch_0 = conv2d_bn(x, 96, 1)
      branch_1 = conv2d_bn(x, 48, 1)
      branch_1 = conv2d_bn(branch_1, 64, 5)
      branch_2 = conv2d_bn(x, 64, 1)
      branch_2 = conv2d_bn(branch_2, 96, 3)
      branch_2 = conv2d_bn(branch_2, 96, 3)
      branch_pool = layers.AveragePooling2D(
          3, strides=1, padding="same"
      )(x)
      branch_pool = conv2d_bn(branch_pool, 64, 1)
      branches = [branch_0, branch_1, branch_2, branch_pool]
      channel_axis = (
          1 if backend.image_data_format() == "channels_first" else 3
      )
      x = layers.Concatenate(axis=channel_axis, name="mixed_5b")(
          branches
      )
  
      # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320
      for block_idx in range(1, 11):
          x = inception_resnet_block(
              x, scale=0.17, block_type="block35", block_idx=block_idx
          )
  
      # Mixed 6a (Reduction-A block): 17 x 17 x 1088
      branch_0 = conv2d_bn(x, 384, 3, strides=2, padding="valid")
      branch_1 = conv2d_bn(x, 256, 1)
      branch_1 = conv2d_bn(branch_1, 256, 3)
      branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding="valid")
      branch_pool = layers.MaxPooling2D(3, strides=2, padding="valid")(
          x
      )
      branches = [branch_0, branch_1, branch_pool]
      x = layers.Concatenate(axis=channel_axis, name="mixed_6a")(
          branches
      )
  
      # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088
      for block_idx in range(1, 21):
          x = inception_resnet_block(
              x, scale=0.1, block_type="block17", block_idx=block_idx
          )
  
      # Mixed 7a (Reduction-B block): 8 x 8 x 2080
      branch_0 = conv2d_bn(x, 256, 1)
      branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding="valid")
      branch_1 = conv2d_bn(x, 256, 1)
      branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding="valid")
      branch_2 = conv2d_bn(x, 256, 1)
      branch_2 = conv2d_bn(branch_2, 288, 3)
      branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding="valid")
      branch_pool = layers.MaxPooling2D(3, strides=2, padding="valid")(
          x
      )
      branches = [branch_0, branch_1, branch_2, branch_pool]
      x = layers.Concatenate(axis=channel_axis, name="mixed_7a")(
          branches
      )
  
      # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080
      for block_idx in range(1, 10):
          x = inception_resnet_block(
              x, scale=0.2, block_type="block8", block_idx=block_idx
          )
      x = inception_resnet_block(
          x,
          scale=1.0,
          activation=None,
          block_type="block8",
          block_idx=10,
      )
  
      # Final convolution block: 8 x 8 x 1536
      x = conv2d_bn(x, 1536, 1, name="conv_7b")
  
      if include_top:
          # Classification block
          x = layers.GlobalAveragePooling2D(name="avg_pool")(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
  
      # Create model.
      model = Functional(inputs, x, name="inception_resnet_v2")
  
      # Load weights.
      if weights == "imagenet":
          if include_top:
              fname = "inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5"
              weights_path = file_utils.get_file(
                  fname,
                  BASE_WEIGHT_URL + fname,
                  cache_subdir="models",
                  file_hash="e693bd0210a403b3192acc6073ad2e96",
              )
          else:
              fname = (
                  "inception_resnet_v2_weights_"
                  "tf_dim_ordering_tf_kernels_notop.h5"
              )
              weights_path = file_utils.get_file(
                  fname,
                  BASE_WEIGHT_URL + fname,
                  cache_subdir="models",
                  file_hash="d19885ff4a710c122648d3b5c3b684e4",
              )
          model.load_weights(weights_path)
      elif weights is not None:
          model.load_weights(weights)
  
      return model
  
file_path: keras/keras/applications/inception_resnet_v2.py
imports: from keras import backend
  from keras import layers
  from keras.api_export import keras_export
  from keras.applications import imagenet_utils
  from keras.layers.layer import Layer
  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: InceptionResNetV2(include_top, weights, input_tensor, input_shape, pooling, classes, classifier_activation) and returns: model