code: def relu(x, negative_slope=0.0, max_value=None, threshold=0.0):
      """Applies the rectified linear unit activation function.
  
      With default values, this returns the standard ReLU activation:
      `max(x, 0)`, the element-wise maximum of 0 and the input tensor.
  
      Modifying default parameters allows you to use non-zero thresholds,
      change the max value of the activation,
      and to use a non-zero multiple of the input for values below the threshold.
  
      Examples:
  
      >>> x = [-10, -5, 0.0, 5, 10]
      >>> keras.activations.relu(x)
      [ 0.,  0.,  0.,  5., 10.]
      >>> keras.activations.relu(x, negative_slope=0.5)
      [-5. , -2.5,  0. ,  5. , 10. ]
      >>> keras.activations.relu(x, max_value=5.)
      [0., 0., 0., 5., 5.]
      >>> keras.activations.relu(x, threshold=5.)
      [-0., -0.,  0.,  0., 10.]
  
      Args:
          x: Input tensor.
          negative_slope: A `float` that controls the slope
              for values lower than the threshold.
          max_value: A `float` that sets the saturation threshold (the largest
              value the function will return).
          threshold: A `float` giving the threshold value of the activation
              function below which values will be damped or set to zero.
  
      Returns:
          A tensor with the same shape and dtype as input `x`.
      """
      if backend.any_symbolic_tensors((x,)):
          return ReLU(
              negative_slope=negative_slope,
              max_value=max_value,
              threshold=threshold,
          )(x)
      return ReLU.static_call(
          x,
          negative_slope=negative_slope,
          max_value=max_value,
          threshold=threshold,
      )
  
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
label_desc: Use image node_id 1 for calling a global function with example usage: relu(x, negative_slope, max_value, threshold) and returns: ReLU, ReLU