code: def inceptionv3(
      input_shape: Tuple[int, int, int],
      weights: Optional[str] = None,
      init_backbone_with_imagenet: bool = True,
      config: Optional[ml_collections.ConfigDict] = None,
  ) -> tf.keras.Model:
      """Returns an InceptionV3 architecture.
  
      See https://tensorflow.org/api_docs/python/tf/keras/applications/InceptionV3.
  
      Args:
        input_shape: a 3-tuple describing the input shape.
        weights: str. To initial weights from.
        init_backbone_with_imagenet: If True, get a model with InceptionV3 that has
          `weights='imagenet'` to start with. This will download a model. It should
          be set to False in unit tests, or when specific model weights will be
          loaded afterwards.
        config: a model configuration.
  
      Returns:
        An InceptionV3-based model.
      """
      backbone = tf.keras.applications.InceptionV3(
          include_top=False,
          weights=None,
          input_shape=input_shape,
          classes=dv_constants.NUM_CLASSES,
          pooling="avg",
      )
  
      if config:
          weight_decay = config.weight_decay
          backbone_dropout_rate = config.backbone_dropout_rate
      else:
          weight_decay = _DEFAULT_WEIGHT_DECAY
          backbone_dropout_rate = _DEFAULT_BACKBONE_DROPOUT_RATE
  
      backbone = add_l2_regularizers(
          backbone, tf.keras.layers.Conv2D, l2=weight_decay
      )
  
      hid = tf.keras.layers.Dropout(backbone_dropout_rate)(
          backbone.output
      )
  
      outputs = []
      outputs.append(build_classification_head(hid, l2=weight_decay))
  
      model = tf.keras.Model(
          inputs=backbone.input, outputs=outputs, name="inceptionv3"
      )
      model.summary()
      logging.info("Number of l2 regularizers: %s.", len(model.losses))
  
      # If no weights file is specified, initialize with `imagenet`.
      # The `init_backbone_with_imagenet` flag should be set to False for unit
      # tests to avoid loading the `imagenet` model from online.
      if not weights and init_backbone_with_imagenet:
          logging.info(
              "inceptionv3: Initiate the model with imagenet (3 channels)."
          )
          model = load_weights_to_model_with_different_channels(
              model, inceptionv3_with_imagenet(input_shape)
          )
          return model
      elif not weights and not init_backbone_with_imagenet:
          logging.info("inceptionv3: No initial checkpoint specified.")
          return model
  
      weights_num_channels = num_channels_from_checkpoint(weights)
      # If the input weights have different number of channels, need some special
      # care:
      model_num_channels = input_shape[2]
      if weights_num_channels != model_num_channels:
          # This step is harder to do directly from `weights`, or even the Checkpoint
          # file format. So, create a `input_model` with expected #chanenls, load
          # the weights, and then post-process.
          # Improve later if possible: find a more readable alternative for this.
          weights_input_shape = list(input_shape)
          weights_input_shape[2] = weights_num_channels
          input_model = inceptionv3(
              tuple(weights_input_shape),
              weights,
              init_backbone_with_imagenet=False,
          )
          logging.info(
              "inceptionv3: Assigning weights from %s channels to %s channels",
              weights_num_channels,
              model_num_channels,
          )
          model = load_weights_to_model_with_different_channels(
              model, input_model
          )
          return model
      else:
          logging.info(
              "inceptionv3: load_weights from checkpoint: %s", weights
          )
          model.load_weights(weights)
          return model
  
imports: import os
  import tempfile
  from typing import Callable, Optional, Tuple, Type, Union
  from absl import logging
  import ml_collections
  import numpy
  import tensorflow
  import tensorflow_addons
  from deepvariant import dv_constants
  from deepvariant import dv_utils
label_desc: Use image node_id 6 for calling a global function with example usage: inceptionv3(input_shape, weights, init_backbone_with_imagenet, config) and returns: model, model, model, model