code: def __init__(
      self, dim=None, dtype=torch.float, eps=1e-5, weight=None
  ):
      super(RMSNormalize, self).__init__()
      if weight is not None:
          self.weight = weight
      else:
          self.weight = nn.Parameter(
              torch.ones(
                  dim,
                  dtype=dtype,
                  device=get_accelerator().current_device_name(),
              )
          )
  
      self.eps = eps
  
imports: import torch
  from deepspeed import comm
  from torch import nn
  from torch.nn import functional
  from torch.nn.parameter import Parameter
  from deepspeed.accelerator import get_accelerator
  from deepspeed.module_inject.tp_shard import get_shard_size, get_shard_size_list
label_desc: Use image node_id 1 to create a new RMSNormalize object from inherited base classes: nn with example: obj = RMSNormalize(dim, dtype, eps, weight)