code: def InceptionResNetV2(
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      **kwargs,
  ):
      """Instantiates the Inception-ResNet v2 architecture.
      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`.
      # Arguments
          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.
      # Returns
          A Keras `Model` instance.
      # Raises
          ValueError: in case of invalid argument for `weights`,
              or invalid input shape.
      """
      global backend, layers, models, keras_utils
      backend, layers, models, keras_utils = get_submodules_from_kwargs(
          kwargs
      )
  
      if not (weights in {"imagenet", None} or os.path.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"
          )
  
      # Determine proper input shape
      input_shape = imagenet_utils._obtain_input_shape(
          input_shape,
          default_size=299,
          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
  
      # Stem block: 35 x 35 x 192
      x = conv2d_bn(img_input, 32, 3, strides=2, padding="same")
      x = conv2d_bn(x, 32, 3, padding="same")
      x = conv2d_bn(x, 64, 3, padding="same")
      x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
      x = conv2d_bn(x, 80, 1, padding="same")
      x = conv2d_bn(x, 192, 3, padding="same")
      x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
  
      # Mixed 5b (Inception-A block): 35 x 35 x 320
      branch_0 = conv2d_bn(x, 96, 1, padding="same")
      branch_1 = conv2d_bn(x, 48, 1, padding="same")
      branch_1 = conv2d_bn(branch_1, 64, 5, padding="same")
      branch_2 = conv2d_bn(x, 64, 1, padding="same")
      branch_2 = conv2d_bn(branch_2, 96, 3, padding="same")
      branch_2 = conv2d_bn(branch_2, 96, 3, padding="same")
      branch_pool = layers.AveragePooling2D(
          3, strides=1, padding="same"
      )(x)
      branch_pool = conv2d_bn(branch_pool, 64, 1, padding="same")
      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="same")
      branch_1 = conv2d_bn(x, 256, 1, padding="same")
      branch_1 = conv2d_bn(branch_1, 256, 3, padding="same")
      branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding="same")
      branch_pool = layers.MaxPooling2D(3, strides=2, padding="same")(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, padding="same")
      branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding="same")
      branch_1 = conv2d_bn(x, 256, 1, padding="same")
      branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding="same")
      branch_2 = conv2d_bn(x, 256, 1, padding="same")
      branch_2 = conv2d_bn(branch_2, 288, 3, padding="same")
      branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding="same")
      branch_pool = layers.MaxPooling2D(3, strides=2, padding="same")(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)
          x = layers.Dense(
              classes, activation="softmax", 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 = keras_utils.get_source_inputs(input_tensor)
      else:
          inputs = img_input
  
      # Create model.
      model = models.Model(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 = keras_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 = keras_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: segmentation_models/segmentation_models/backbones/inception_resnet_v2.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  from keras_applications import imagenet_utils
  from keras_applications import get_submodules_from_kwargs
label_desc: Use image node_id 4 for calling a global function with example usage: InceptionResNetV2(include_top, weights, input_tensor, input_shape, pooling, classes) and returns: model