code: def layer_test(
      layer_cls,
      kwargs=None,
      batch_input_shape=None,
      batch_input_data=None,
  ):
      """Test routine for a layer with a single input and single output.
      Arguments:
        layer_cls: Layer class object.
        kwargs: Optional dictionary of keyword arguments for instantiating the
          layer.
        input_shape: Input shape tuple.
        input_dtype: Data type of the input data.
        input_data: Numpy array of input data.
      Returns:
        The output data (Numpy array) returned by the layer, for additional
        checks to be done by the calling code.
      Raises:
        ValueError: if `input_data is None and input_shape is None`.
      """
      batch_input_shape, batch_input_data = _sanitize_testing_args(
          batch_input_shape, batch_input_data
      )
  
      # instantiation
      kwargs = kwargs or {}
  
      with tf.name_scope("TFE"):
          layer = layer_cls(
              batch_input_shape=batch_input_shape, **kwargs
          )
          model = Sequential()
          model.add(layer)
  
          x = tfe.define_private_variable(batch_input_data)
          model(x)
  
imports: import numpy
  import tensorflow
  import tf_encrypted
  from tf_encrypted.keras.models import Sequential
label_desc: Use image node_id 2 for calling a global function with example usage: layer_test(layer_cls, kwargs, batch_input_shape, batch_input_data) without return types