class_docstr: Rectified Linear Unit activation function.
  With default values, it returns element-wise `max(x, 0)`.
  Otherwise, it follows:
  `f(x) = max_value` for `x >= max_value`,
  `f(x) = x` for `threshold <= x < max_value`,
  `f(x) = negative_slope * (x - threshold)` otherwise.
  Input shape:
      Arbitrary. Use the keyword argument `input_shape`
      (tuple of integers, does not include the samples axis)
      when using this layer as the first layer in a model.
  Output shape:
      Same shape as the input.
  Arguments:
      max_value: float >= 0. Maximum activation value.
      negative_slope: float >= 0. Negative slope coefficient.
      threshold: float. Threshold value for thresholded activation.
code: def compute_output_shape(self, input_shape):
      return input_shape
  
file_path: tf-encrypted/tf_encrypted/keras/layers/relu.py
imports: import tf_encrypted
  from tf_encrypted.keras.engine import Layer
  from tf_encrypted.keras.layers.layers_utils import default_args_check
label_desc: Use image node_id 5 for calling the ReLU obj's underlying member method code with example usage: obj.compute_output_shape(input_shape) and returns: input_shape