normalization during evaluation. The running estimates are kept
   with a default "momentum" of 0.1.
If "track_running_stats" is set to "False", this layer then does
   not keep running estimates, and batch statistics are instead used
   during evaluation time as well.
Note:
 This "momentum" argument is different from one used in optimizer
 classes and the conventional notion of momentum. Mathematically,
 the update rule for running statistics here is \hat{x}_\text{new}
 = (1 - \text{momentum}) \times \hat{x} + \text{momentum} \times
 x_t, where \hat{x} is the estimated statistic and x_t is the new
 observed value.

Because the Batch Normalization is done over the C dimension,
   computing statistics on (N, L) slices, it's common terminology to
   call this Temporal Batch Normalization.
Parameters:
      * num_features (int) -- number of features or channels C
        of the input
  * **eps** (*float*) -- a value added to the denominator for
