docstr: Shards the second MoE param and passes it to the underlying implementation. See the above for context on why this API
  exists.
  
  This will return `None` for expert bias params not on TP rank 0. NOTE(cmikeh2): Does it make sense to round-robin assign?
  My intuition is that this will make debugging much more difficult for minimal memory reduction.
  
  Args:
      param (torch.Tensor): The parameter to transform. This should have shape (n_experts, out_neurons, in_neurons).
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code: def transform_moe_mlp_2_param(
      self, param: torch.Tensor
  ) -> Optional[torch.Tensor]:
      """
      Shards the second MoE param and passes it to the underlying implementation. See the above for context on why this API
      exists.
  
      This will return `None` for expert bias params not on TP rank 0. NOTE(cmikeh2): Does it make sense to round-robin assign?
      My intuition is that this will make debugging much more difficult for minimal memory reduction.
  
      Args:
          param (torch.Tensor): The parameter to transform. This should have shape (n_experts, out_neurons, in_neurons).
      """
      param = shard_mlp_2_param(
          param, self.tp_rank, self.tp_size, is_moe=True
      )
  
      if param is not None:
          param = self.moe.transform_moe_mlp_2_param(param)
  
      return param
  
imports: from abc import abstractmethod
  from typing import Optional
  import torch
  from deepspeed.accelerator import get_accelerator
  from ..config_v2 import RaggedInferenceEngineConfig
  from ..inference_utils import ActivationType, ceil_div, is_gated
  from ..model_implementations import *
  from ..model_implementations.sharding import *
  from ..modules.configs import DSEmbeddingsConfig, DSLinearConfig, DSMoEConfig, DSNormConfig, DSSelfAttentionConfig, DSUnembedConfig, NormTypeEnum, PositionalEmbeddingType
  from ..modules import heuristics
  from ..ragged import DSSequenceDescriptor, KVCacheConfig, RaggedBatchWrapper
  from .inference_model_base import DSInferenceModelBase, DSModelImplementationConfig, MPType
  from ..inference_parameter import InferenceParameter
label_desc: Use image node_id 7 for calling the DSMoETransformerModelBase obj's underlying member method code with example usage: obj.transform_moe_mlp_2_param(param) and returns: param