class_docstr: Rectified Linear Unit activation function layer.
  
  Formula:
  ``` python
  f(x) = max(x,0)
  f(x) = max_value if x >= max_value
  f(x) = x if threshold <= x < max_value
  f(x) = negative_slope * (x - threshold) otherwise
  ```
  
  Example:
  ``` python
  relu_layer = keras.layers.activations.ReLU(
      max_value=10,
      negative_slope=0.5,
      threshold=0,
  )
  input = np.array([-10, -5, 0.0, 5, 10])
  result = relu_layer(input)
  # result = [-5. , -2.5,  0. ,  5. , 10.]
  ```
  
  Args:
      max_value: Float >= 0. Maximum activation value. None means unlimited.
          Defaults to `None`.
      negative_slope: Float >= 0. Negative slope coefficient.
          Defaults to `0.0`.
      threshold: Float >= 0. Threshold value for thresholded activation.
          Defaults to `0.0`.
      **kwargs: Base layer keyword arguments, such as `name` and `dtype`.
code: def call(self, inputs):
      return activations.relu(
          inputs,
          negative_slope=self.negative_slope,
          max_value=self.max_value,
          threshold=self.threshold,
      )
  
file_path: keras/keras/layers/activations/relu.py
imports: from keras import activations
  from keras.api_export import keras_export
  from keras.layers.layer import Layer
label_desc: Use image node_id 2 for calling the ReLU obj's underlying member method code with example usage: obj.call(inputs) and returns: activations