class_docstr: Rectified Linear Unit activation function.
  
  With default values, it returns element-wise `max(x, 0)`.
  
  Otherwise, it follows:
  
  ```
    f(x) = max_value if x >= max_value
    f(x) = x if threshold <= x < max_value
    f(x) = negative_slope * (x - threshold) otherwise
  ```
  
  Usage:
  
  >>> layer = tf.keras.layers.ReLU()
  >>> output = layer([-3.0, -1.0, 0.0, 2.0])
  >>> list(output.numpy())
  [0.0, 0.0, 0.0, 2.0]
  >>> layer = tf.keras.layers.ReLU(max_value=1.0)
  >>> output = layer([-3.0, -1.0, 0.0, 2.0])
  >>> list(output.numpy())
  [0.0, 0.0, 0.0, 1.0]
  >>> layer = tf.keras.layers.ReLU(negative_slope=1.0)
  >>> output = layer([-3.0, -1.0, 0.0, 2.0])
  >>> list(output.numpy())
  [-3.0, -1.0, 0.0, 2.0]
  >>> layer = tf.keras.layers.ReLU(threshold=1.5)
  >>> output = layer([-3.0, -1.0, 1.0, 2.0])
  >>> list(output.numpy())
  [0.0, 0.0, 0.0, 2.0]
  
  Input shape:
    Arbitrary. Use the keyword argument `input_shape`
    (tuple of integers, does not include the batch axis)
    when using this layer as the first layer in a model.
  
  Output shape:
    Same shape as the input.
  
  Args:
    max_value: Float >= 0. Maximum activation value. Default to None, which
      means unlimited.
    negative_slope: Float >= 0. Negative slope coefficient. Default to 0.
    threshold: Float >= 0. Threshold value for thresholded activation. Default
      to 0.
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code: def get_config(self):
      config = {
          "max_value": self.max_value,
          "negative_slope": self.negative_slope,
          "threshold": self.threshold,
      }
      base_config = super(ReLU, self).get_config()
      return dict(list(base_config.items()) + list(config.items()))
  
imports: from tensorflow.python.framework import dtypes
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import constraints
  from tensorflow.python.keras import initializers
  from tensorflow.python.keras import regularizers
  from tensorflow.python.keras.engine.base_layer import Layer
  from tensorflow.python.keras.engine.input_spec import InputSpec
  from tensorflow.python.keras.utils import tf_utils
  from tensorflow.python.ops import math_ops
label_desc: Use image node_id 3 for calling the ReLU obj's underlying member method code with example usage: obj.get_config() and returns: dict