docstr: Performs a single optimization step.
docstr_tok: ["Performs","a","single","optimization","step","."]
code: def step(self, closure=None):
      """Performs a single optimization step."""
      self._sync_fp16_grads_to_fp32()
      self.fp32_optimizer.step(closure)
  
      # copy FP32 params back into FP16 model
      offset = 0
      for p in self.fp16_params:
          if not p.requires_grad:
              continue
          numel = p.data.numel()
          p.data.copy_(
              self.fp32_params.data[offset : offset + numel].view_as(
                  p.data
              )
          )
          offset += numel
  
imports: from itertools import chain
  import torch
  from fairseq import utils
  from fairseq.optim.fp16_optimizer import DynamicLossScaler
label_desc: Use image node_id 9 for calling the Fairseq_FP16OptimizerMixin obj's underlying member method code with example usage: obj.step(closure) without return types