docstr: Convert a native PyTorch layer normalization module to a specific layer normalization module,
  and optionally mark parameters for gradient aggregation.
  
  Args:
      module (nn.Module): The native PyTorch layer normalization module to be converted.
      sp_partial_derived (bool): Whether this module's gradients are partially derived in sequence parallelism.
  
  Returns:
      nn.Module: The specific layer normalization module.
  
  Raises:
      AssertionError: If the provided module is not an instance of the supported layer normalization type.
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code: def from_native_module(
      module: nn.Module, sp_partial_derived: bool = False
  ):
      """
      Convert a native PyTorch layer normalization module to a specific layer normalization module,
      and optionally mark parameters for gradient aggregation.
  
      Args:
          module (nn.Module): The native PyTorch layer normalization module to be converted.
          sp_partial_derived (bool): Whether this module's gradients are partially derived in sequence parallelism.
  
      Returns:
          nn.Module: The specific layer normalization module.
  
      Raises:
          AssertionError: If the provided module is not an instance of the supported layer normalization type.
      """
  
imports: import warnings
  from abc import ABC, abstractmethod
  import torch.nn
  from colossalai.lazy import LazyInitContext
  from ._operation import hook_paramter_in_backward
  from .utils import SeqParallelUtils
label_desc: Use image node_id 1 for calling the BaseLayerNorm obj's underlying member method code with example usage: obj.from_native_module(module, sp_partial_derived) without return types