code: def load_weights_to_model_with_different_channels(
      model: tf.keras.Model, input_model: tf.keras.Model
  ) -> tf.keras.Model:
      """Initialize `model` with weights from `input_model` (different #channels).
  
      Args:
        model: The model we want to output.
        input_model: The input model that contains the weights to initialize from.
  
      Returns:
        `model` with updated weights from `input_model`
      """
      for layer_i, (input_model_layer, new_layer) in enumerate(
          zip(input_model.layers, model.layers)
      ):
          if not new_layer.weights:
              continue
          if len(new_layer.weights) != len(input_model_layer.weights):
              raise ValueError(
                  "We expect input weights and the model we train to both "
                  "be InceptionV3. The top level dict should be the same."
              )
  
          # Create a list of ndarray, which will be used input for `set_weights`
          # at the end.
          new_weights_to_assign = new_layer.get_weights()
  
          for i, (
              input_model_layer_weights,
              new_layer_weights,
          ) in enumerate(
              zip(
                  input_model_layer.get_weights(),
                  new_layer.get_weights(),
              )
          ):
              if (
                  input_model_layer_weights.shape
                  == new_layer_weights.shape
              ):
                  new_weights_to_assign[i] = input_model_layer_weights
              else:
                  logging.info(
                      (
                          "input weights file layer %s:%s has shape %s, "
                          "target model layer %s:%s has shape %s"
                      ),
                      input_model_layer.name,
                      i,
                      input_model_layer_weights.shape,
                      new_layer.name,
                      i,
                      new_layer_weights.shape,
                  )
                  min_num_channels = min(
                      input_model_layer_weights.shape[2],
                      new_layer_weights.shape[2],
                  )
                  new_weights_to_assign[i][
                      :, :, :min_num_channels, :
                  ] = input_model_layer_weights[
                      :, :, :min_num_channels, :
                  ]
          # Now that the new_layer_weights list has the value we want to load with,
          # and has the right shape.
          model.layers[layer_i].set_weights(new_weights_to_assign)
  
      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 3 for calling a global function with example usage: load_weights_to_model_with_different_channels(model, input_model) and returns: model