code: def InceptionV3(
      include_top=True,
      weights="imagenet",
      input_tensor=None,
      input_shape=None,
      pooling=None,
      classes=1000,
      **kwargs,
  ):
      """Instantiates the Inception v3 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=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
  
      if backend.image_data_format() == "channels_first":
          channel_axis = 1
      else:
          channel_axis = 3
  
      x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding="same")
      x = conv2d_bn(x, 32, 3, 3, padding="same")
      x = conv2d_bn(x, 64, 3, 3, padding="same")
      x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
  
      x = conv2d_bn(x, 80, 1, 1, padding="same")
      x = conv2d_bn(x, 192, 3, 3, padding="same")
      x = layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
  
      # mixed 0: 35 x 35 x 256
      branch1x1 = conv2d_bn(x, 64, 1, 1)
  
      branch5x5 = conv2d_bn(x, 48, 1, 1)
      branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
      branch3x3dbl = conv2d_bn(x, 64, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
      branch_pool = layers.AveragePooling2D(
          (3, 3), strides=(1, 1), padding="same"
      )(x)
      branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch5x5, branch3x3dbl, branch_pool],
          axis=channel_axis,
          name="mixed0",
      )
  
      # mixed 1: 35 x 35 x 288
      branch1x1 = conv2d_bn(x, 64, 1, 1)
  
      branch5x5 = conv2d_bn(x, 48, 1, 1)
      branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
      branch3x3dbl = conv2d_bn(x, 64, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
      branch_pool = layers.AveragePooling2D(
          (3, 3), strides=(1, 1), padding="same"
      )(x)
      branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch5x5, branch3x3dbl, branch_pool],
          axis=channel_axis,
          name="mixed1",
      )
  
      # mixed 2: 35 x 35 x 288
      branch1x1 = conv2d_bn(x, 64, 1, 1)
  
      branch5x5 = conv2d_bn(x, 48, 1, 1)
      branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
  
      branch3x3dbl = conv2d_bn(x, 64, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
  
      branch_pool = layers.AveragePooling2D(
          (3, 3), strides=(1, 1), padding="same"
      )(x)
      branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch5x5, branch3x3dbl, branch_pool],
          axis=channel_axis,
          name="mixed2",
      )
  
      # mixed 3: 17 x 17 x 768
      branch3x3 = conv2d_bn(
          x, 384, 3, 3, strides=(2, 2), padding="same"
      )
  
      branch3x3dbl = conv2d_bn(x, 64, 1, 1)
      branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
      branch3x3dbl = conv2d_bn(
          branch3x3dbl, 96, 3, 3, strides=(2, 2), padding="same"
      )
  
      branch_pool = layers.MaxPooling2D(
          (3, 3), strides=(2, 2), padding="same"
      )(x)
      x = layers.concatenate(
          [branch3x3, branch3x3dbl, branch_pool],
          axis=channel_axis,
          name="mixed3",
      )
  
      # mixed 4: 17 x 17 x 768
      branch1x1 = conv2d_bn(x, 192, 1, 1)
  
      branch7x7 = conv2d_bn(x, 128, 1, 1)
      branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
      branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
      branch7x7dbl = conv2d_bn(x, 128, 1, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
      branch_pool = layers.AveragePooling2D(
          (3, 3), strides=(1, 1), padding="same"
      )(x)
      branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch7x7, branch7x7dbl, branch_pool],
          axis=channel_axis,
          name="mixed4",
      )
  
      # mixed 5, 6: 17 x 17 x 768
      for i in range(2):
          branch1x1 = conv2d_bn(x, 192, 1, 1)
  
          branch7x7 = conv2d_bn(x, 160, 1, 1)
          branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
          branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
          branch7x7dbl = conv2d_bn(x, 160, 1, 1)
          branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
          branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
          branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
          branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
          branch_pool = layers.AveragePooling2D(
              (3, 3), strides=(1, 1), padding="same"
          )(x)
          branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
          x = layers.concatenate(
              [branch1x1, branch7x7, branch7x7dbl, branch_pool],
              axis=channel_axis,
              name="mixed" + str(5 + i),
          )
  
      # mixed 7: 17 x 17 x 768
      branch1x1 = conv2d_bn(x, 192, 1, 1)
  
      branch7x7 = conv2d_bn(x, 192, 1, 1)
      branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
      branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
  
      branch7x7dbl = conv2d_bn(x, 192, 1, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
      branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
  
      branch_pool = layers.AveragePooling2D(
          (3, 3), strides=(1, 1), padding="same"
      )(x)
      branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
      x = layers.concatenate(
          [branch1x1, branch7x7, branch7x7dbl, branch_pool],
          axis=channel_axis,
          name="mixed7",
      )
  
      # mixed 8: 8 x 8 x 1280
      branch3x3 = conv2d_bn(x, 192, 1, 1)
      branch3x3 = conv2d_bn(
          branch3x3, 320, 3, 3, strides=(2, 2), padding="same"
      )
  
      branch7x7x3 = conv2d_bn(x, 192, 1, 1)
      branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
      branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
      branch7x7x3 = conv2d_bn(
          branch7x7x3, 192, 3, 3, strides=(2, 2), padding="same"
      )
  
      branch_pool = layers.MaxPooling2D(
          (3, 3), strides=(2, 2), padding="same"
      )(x)
      x = layers.concatenate(
          [branch3x3, branch7x7x3, branch_pool],
          axis=channel_axis,
          name="mixed8",
      )
  
      # mixed 9: 8 x 8 x 2048
      for i in range(2):
          branch1x1 = conv2d_bn(x, 320, 1, 1)
  
          branch3x3 = conv2d_bn(x, 384, 1, 1)
          branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
          branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
          branch3x3 = layers.concatenate(
              [branch3x3_1, branch3x3_2],
              axis=channel_axis,
              name="mixed9_" + str(i),
          )
  
          branch3x3dbl = conv2d_bn(x, 448, 1, 1)
          branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
          branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
          branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
          branch3x3dbl = layers.concatenate(
              [branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis
          )
  
          branch_pool = layers.AveragePooling2D(
              (3, 3), strides=(1, 1), padding="same"
          )(x)
          branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
          x = layers.concatenate(
              [branch1x1, branch3x3, branch3x3dbl, branch_pool],
              axis=channel_axis,
              name="mixed" + str(9 + i),
          )
      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_v3")
  
      # Load weights.
      if weights == "imagenet":
          if include_top:
              weights_path = keras_utils.get_file(
                  "inception_v3_weights_tf_dim_ordering_tf_kernels.h5",
                  WEIGHTS_PATH,
                  cache_subdir="models",
                  file_hash="9a0d58056eeedaa3f26cb7ebd46da564",
              )
          else:
              weights_path = keras_utils.get_file(
                  "inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5",
                  WEIGHTS_PATH_NO_TOP,
                  cache_subdir="models",
                  file_hash="bcbd6486424b2319ff4ef7d526e38f63",
              )
          model.load_weights(weights_path)
      elif weights is not None:
          model.load_weights(weights)
  
      return model
  
file_path: segmentation_models/segmentation_models/backbones/inception_v3.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 2 for calling a global function with example usage: InceptionV3(include_top, weights, input_tensor, input_shape, pooling, classes) and returns: model