code: def configure():
      memory_growth = os.environ.get("MEMORY_GROWTH", False)
      memory_allocated = os.environ.get("MEMORY_ALLOCATED", False)
  
      if memory_growth:
          gpus = tf.config.experimental.list_physical_devices("GPU")
          if gpus:
              try:
                  for gpu in gpus:
                      tf.config.experimental.set_memory_growth(
                          gpu, True
                      )
              except RuntimeError as e:
                  raise e
          else:
              print("Memory growth set but no GPUs detected")
  
      if memory_allocated and isinstance(memory_allocated, float):
          config = tf.compat.v1.ConfigProto()
          config.gpu_options.per_process_gpu_memory_fraction = (  # pylint: disable=no-member
              memory_allocated
          )
          tf.compat.v1.keras.backend.set_session(
              tf.compat.v1.Session(config=config)
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: configure() without return types