class_docstr: Internal wrapper around a torch optimizer.
  
  Conditionally will perform `step` and `zero_grad` if gradients should be synchronized when performing gradient
  accumulation.
  
  Args:
      optimizer (`torch.optim.optimizer.Optimizer`):
          The optimizer to wrap.
      device_placement (`bool`, *optional*, defaults to `True`):
          Whether or not the optimizer should handle device placement. If so, it will place the state dictionary of
          `optimizer` on the right device.
      scaler (`torch.cuda.amp.grad_scaler.GradScaler`, *optional*):
          The scaler to use in the step function if training with mixed precision.
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code: def param_groups(self, param_groups):
      self.optimizer.param_groups = param_groups
  
imports: import inspect
  import warnings
  import torch
  from .state import AcceleratorState, GradientState
  from .utils import DistributedType, honor_type, is_tpu_available
label_desc: Use image node_id 5 for calling the AcceleratedOptimizer obj's underlying member method code with example usage: obj.param_groups(param_groups) without return types