code: def inceptionv3_with_imagenet(
      input_shape: Tuple[int, int, int]
  ) -> tf.keras.Model:
      """Returns `inceptionv3` model with 3 channels; init with `weights=imagenet`.
  
      Our `inceptionv3` model (defined in this file as well) allows #channels other
      than 3. When the #channels is not 3, we couldn't set `weights=imagenet` to
      our backbone tf.keras.applications.InceptionV3 because it will complain the
      number of channels is not 3. We created this "inceptionv3_with_imagenet"
      model which has the same architecture, which we can then use our own
      "load_weights_to_model_with_different_channels" function to initiate an
      inceptionv3 model with any numbers of channels.
  
      The reason why this function is separate (instead of parameterized in the same
      implementation of inceptionv3) is to make it easier to read.
  
      Args:
        input_shape: a 3-tuple describing the input shape. The 3rd dimension is not
          used in this function. We always set that to 3 in this function.
  
      Returns:
        An InceptionV3-based model with 3 channels and init with `weights=imagenet`.
      """
      input_shape = list(input_shape)
      input_shape = [input_shape[0], input_shape[1], 3]
  
      backbone = tf.keras.applications.InceptionV3(
          include_top=False,
          weights="imagenet",
          input_shape=input_shape,
          classes=dv_constants.NUM_CLASSES,
          pooling="avg",
      )
  
      weight_decay = _DEFAULT_WEIGHT_DECAY
      backbone = add_l2_regularizers(
          backbone, tf.keras.layers.Conv2D, l2=weight_decay
      )
      backbone_drop_rate = _DEFAULT_BACKBONE_DROPOUT_RATE
  
      hid = tf.keras.layers.Dropout(backbone_drop_rate)(backbone.output)
  
      outputs = []
      outputs.append(build_classification_head(hid, l2=weight_decay))
  
      model = tf.keras.Model(
          inputs=backbone.input, outputs=outputs, name="inceptionv3"
      )
      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 5 for calling a global function with example usage: inceptionv3_with_imagenet(input_shape) and returns: model