docstr: Clips gradient norm and updates dynamic loss scaler.
code: def clip_main_grads(self, max_norm):
      """
      Clips gradient norm and updates dynamic loss scaler.
      """
      self._sync_fp16_grads_to_fp32()
      grad_norm = clip_grad_norm(
          self.fp32_params, max_norm, sync=self._aggregate_gnorms
      )
  
      # detect overflow and adjust loss scale
      if self.scaler is not None:
          overflow = has_overflow(grad_norm)
          prev_scale = self.scaler.loss_scale
          self.scaler.update_scale(overflow)
          if overflow:
              self.zero_grad()
              if self.scaler.loss_scale <= self.min_loss_scale:
                  # Use FloatingPointError as an uncommon error that parent
                  # functions can safely catch to stop training.
                  self.scaler.loss_scale = prev_scale
                  raise FloatingPointError(
                      (
                          "Minimum loss scale reached ({}). Your loss is probably exploding. "
                          "Try lowering the learning rate, using gradient clipping or "
                          "increasing the batch size."
                      ).format(self.min_loss_scale)
                  )
                  logging.info(
                      f"Overflow: setting loss scale to {self.scaler.loss_scale}"
                  )
  
      return grad_norm
  
imports: import math
  from itertools import chain
  from typing import Optional
  import parlai.utils.logging
label_desc: Use image node_id 10 for calling the SafeFP16Optimizer obj's underlying member method code with example usage: obj.clip_main_grads(max_norm) and returns: grad_norm