\widehat{m_t}/          \big(\sqrt{\widehat{v_t}^{max}} +
      \epsilon \big)                                 \
      &\hspace{5mm}\textbf{else}
      \      &\hspace{10mm}\theta_t \leftarrow \theta_{t-1} - \gamma
      \widehat{m_t}/          \big(\sqrt{\widehat{v_t}} + \epsilon
      \big)                                       \
      &\rule{110mm}{0.4pt}
      \[-1.ex]      &\bf{return} \:  \theta_t
      \[-1.ex]      &\rule{110mm}{0.4pt}
      \[-1.ex] \end{aligned}
For further details regarding the algorithm we refer to Adam: A
   Method for Stochastic Optimization.
Parameters:
      * params (iterable) -- iterable of parameters to optimize
        or dicts defining parameter groups
  * **lr** (*float**, **optional*) -- learning rate (default:
    1e-3)

  * **betas** (*Tuple**[**float**, **float**]**, **optional*) --
    coefficients used for computing running averages of gradient
    and its square (default: (0.9, 0.999))
