class_docstr: Dimensioning properties
class_docstr_tok: ["Dimensioning","properties"]
docstr: Shards the first MLP parameter and passes it to the underlying implementation
  for any necessary transformations.
  
  Args:
      param (torch.Tensor): The parameter to transform. This may be either a bias or weight and should have
          the shape (out_neurons, in_neurons).
docstr_tok: ["Shards","the","first","MLP","parameter","and","passes","it","to","the","underlying","implementation","for","any","necessary","transformations",".","Args",":","param","(","torch.Tensor",")",":","The","parameter","to","transform",".","This","may","be","either","a","bias","or","weight","and","should","have","the","shape","(","out_neurons",",","in_neurons",")","."]
code: def transform_mlp_1_param(
      self, param: torch.Tensor
  ) -> InferenceParameter:
      """
      Shards the first MLP parameter and passes it to the underlying implementation
      for any necessary transformations.
  
      Args:
          param (torch.Tensor): The parameter to transform. This may be either a bias or weight and should have
              the shape (out_neurons, in_neurons).
      """
      param = shard_mlp_1_param(
          param, self.tp_rank, self.tp_size, gated=self.gated_mlp
      )
  
      return self.mlp_1.transform_param(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 31 for calling the DSTransformerModelBase obj's underlying member method code with example usage: obj.transform_mlp_1_param(param) and returns: self