code: def load_params(
      self, module, sd, weight_quantizer, mp_replace, prefix
  ):
      param_names = (
          "self_attn.q_proj.weight",
          "self_attn.k_proj.weight",
          "self_attn.v_proj.weight",
          "self_attn.o_proj.weight",
          "mlp.up_proj.weight",
          "mlp.gate_proj.weight",
          "mlp.down_proj.weight",
          "input_layernorm.weight",
          "post_attention_layernorm.weight" "self_attn.q_proj.bias",
          "self_attn.k_proj.bias",
          "self_attn.v_proj.bias",
          "self_attn.o_proj.bias",
      )
  
      maybe_copy_qkv(
          module.attention,
          sd,
          weight_quantizer,
          mp_replace,
          "attn_qkvw",
          [
              prefix + param_names[0],
              prefix + param_names[1],
              prefix + param_names[2],
          ],
          split_qkv=self.policy.split_qkv,
      )
      maybe_copy_qkv(
          module.attention,
          sd,
          weight_quantizer,
          mp_replace,
          "attn_qkvb",
          [
              prefix + param_names[9],
              prefix + param_names[10],
              prefix + param_names[11],
          ],
          split_qkv=self.policy.split_qkv,
      )
      maybe_copy(
          module.attention,
          sd,
          weight_quantizer,
          mp_replace,
          transformer_param_names[2],
          prefix + param_names[3],
      )
      maybe_copy(
          module.attention,
          sd,
          weight_quantizer,
          mp_replace,
          transformer_param_names[3],
          prefix + param_names[12],
      )
      maybe_copy_geglu(
          module.mlp,
          sd,
          weight_quantizer,
          mp_replace,
          "inter_w",
          [prefix + param_names[4], prefix + param_names[5]],
      )
      maybe_copy(
          module.mlp,
          sd,
          weight_quantizer,
          mp_replace,
          "output_w",
          prefix + param_names[6],
      )
  
      maybe_copy(
          module.mlp,
          sd,
          weight_quantizer,
          mp_replace,
          transformer_param_names[8],
          prefix + param_names[7],
      )
      maybe_copy(
          module,
          sd,
          weight_quantizer,
          mp_replace,
          transformer_param_names[10],
          prefix + param_names[8],
      )
  
imports: import importlib
  import torch
  from torch.nn.parameter import Parameter
  from deepspeed.model_implementations.transformers.ds_gpt import DeepSpeedGPTInference
  from deepspeed.utils.types import ActivationFuncType, NormType
  from ..policy import TransformerPolicy, maybe_copy, maybe_copy_geglu, maybe_copy_qkv, maybe_get_lora, transformer_param_names
  from .base import *
  from .features import HybridGatedMLPContainer, HybridSplitQKVContainer
label_desc: Use image node_id 7 for calling the DS_InternLMContainer obj's underlying member method code with example usage: obj.load_params(module, sd, weight_quantizer, mp_replace, prefix) without return types