code: def num_channels_from_checkpoint(filepath: str) -> int:
      """Determine the number of channels from a checkpoint path."""
      reader = tf.train.load_checkpoint(filepath)
  
      # Loop over variables in the checkpoint.
      for name in reader.get_variable_to_shape_map().keys():
          # 'layer_with_weights-0/kernel/.ATTRIBUTES/VARIABLE_VALUE' seems to the main
          # variable to look at. I'm not sure if this heuristics will always work.
          # TODO
          if name.startswith(
              "layer_with_weights-0/kernel/.ATTRIBUTES/VARIABLE_VALUE"
          ):
              weight_tensor = reader.get_tensor(name)
              return weight_tensor.shape[2]
          # We used to wire the model a bit differently.
          # If we see 'layer_with_weights-0/layer_with_weights-0/kernel', stop and
          # alert the user.
          if name.startswith(
              "layer_with_weights-0/layer_with_weights-0/kernel"
          ):
              raise ValueError(
                  "You are using an older DeepVariant Keras model "
                  "architecture. Please use a new model."
              )
      raise ValueError("Unexpected model format.")
  
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 4 for calling a global function with example usage: num_channels_from_checkpoint(filepath) and returns: weight_tensor