update(new_scale=None)
  Updates the scale factor.

  If any optimizer steps were skipped the scale is multiplied by
  "backoff_factor" to reduce it. If "growth_interval" unskipped
  iterations occurred consecutively, the scale is multiplied by
  "growth_factor" to increase it.

  Passing "new_scale" sets the new scale value manually.
  ("new_scale" is not used directly, it's used to fill
  GradScaler's internal scale tensor. So if "new_scale" was a
  tensor, later in-place changes to that tensor will not further
  affect the scale GradScaler uses internally.)

  Parameters:
     **new_scale** (float or "torch.cuda.FloatTensor", optional,
     default=None) -- New scale factor.

  Warning:

    "update()" should only be called at the end of the iteration,
    after "scaler.step(optimizer)" has been invoked for all
    optimizers used this iteration.

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