code: def create_ds_model_config(self):
      self.set_hidden_heads(*self.policy.get_hidden_heads())
      assert self.num_attention_heads % self.mp_size == 0, (
          "To run the model parallel across the GPUs, the attention_heads require to be divisible by the world_size!"
          + "This is because the attention computation is partitioned evenly among the parallel GPUs."
      )
  
      self.ds_model_config = DeepSpeedInferenceConfig(
          hidden_size=self.hidden_size,
          intermediate_size=self.intermediate_size,
          heads=self.num_attention_heads,
          layer_norm_eps=self.layernorm_epsilon,
          dtype=self.dtype,
          pre_layer_norm=self.pre_layer_norm,
          norm_type=self.norm_type,
          mp_size=self.mp_size,
          return_tuple=self.return_tuple,
          triangular_masking=self.triangular_masking,
          local_attention=self.local_attention,
          window_size=self.window_size,
          rotary_dim=self.rotary_dim,
          mlp_after_attn=self.mlp_after_attn,
          mlp_act_func_type=self.mlp_act_func_type,
          training_mp_size=self.training_mp_size,
          bigscience_bloom=self.bigscience_bloom,
          max_out_tokens=self.max_out_tokens,
          min_out_tokens=self.min_out_tokens,
          scale_attn_by_inverse_layer_idx=self.scale_attn_by_inverse_layer_idx,
          use_mup=self.use_mup,
          return_single_tuple=self.return_single_tuple,
          set_empty_params=self.config.set_empty_params,
          transposed_mode=self.config.transposed_mode,
          use_triton=self.use_triton,
          triton_autotune=self.config.triton_autotune,
      )
  
      if self.use_triton and deepspeed.HAS_TRITON:
          from .bert import DS_BERTContainer
  
          if not isinstance(self, DS_BERTContainer):
              raise NotImplementedError(
                  "Triton kernels are only for BERT-like models yet"
              )
  
          if not self.config.triton_autotune:
              from deepspeed.ops.transformer.inference.triton.matmul_ext import (
                  fp16_matmul,
              )
  
              fp16_matmul.skip_autotune()
  
      return self.ds_model_config
  
imports: from abc import ABC
  import torch
  import deepspeed
  from deepspeed.ops.transformer.inference.config import DeepSpeedInferenceConfig
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 2 for calling the BaseTransformerContainer obj's underlying member method code with example usage: obj.create_ds_model_config() and returns: self