RReLU
class torch.nn.RReLU(lower=0.125, upper=0.3333333333333333, inplace=False)
Applies the randomized leaky rectified liner unit function,
   element-wise, as described in the paper:
Empirical Evaluation of Rectified Activations in Convolutional
   Network.
The function is defined as:
  \text{RReLU}(x) = \begin{cases}     x & \text{if } x \geq 0 \\
  ax & \text{ otherwise } \end{cases}

where a is randomly sampled from uniform distribution
   \mathcal{U}(\text{lower}, \text{upper}).
  See: https://arxiv.org/pdf/1505.00853.pdf

Parameters:
      * lower (float) -- lower bound of the uniform
        distribution. Default: \frac{1}{8}
  * **upper** (*float*) -- upper bound of the uniform
    distribution. Default: \frac{1}{3}

  * **inplace** (*bool*) -- can optionally do the operation in-
    place. Default: "False"

Shape:
      * Input: (*), where * means any number of dimensions.
  * Output: (*), same shape as the input.

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