code: def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
      return (
          tensor.view(bsz, seq_len, self.num_heads, self.head_dim)
          .transpose(1, 2)
          .contiguous()
      )
  
imports: import math
  import os
  import types
  from collections import defaultdict
  from functools import partial
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from torch_int.nn.bmm import BMM_S8T_S8N_F32T, BMM_S8T_S8N_S8T
  from transformers import PreTrainedModel
  from transformers.modeling_outputs import BaseModelOutputWithPast
  from transformers.models.llama.configuration_llama import LlamaConfig
  from transformers.models.llama.modeling_llama import LLAMA_INPUTS_DOCSTRING, LlamaAttention, LlamaDecoderLayer, LlamaMLP, LlamaRotaryEmbedding, repeat_kv, rotate_half
  from transformers.utils import add_start_docstrings_to_model_forward
  from colossalai.inference.tensor_parallel.batch_infer_state import BatchInferState
  from colossalai.kernel.triton import copy_kv_cache_to_dest, int8_rotary_embedding_fwd, smooth_llama_context_attn_fwd, smooth_token_attention_fwd
  from .base_model import BaseSmoothForCausalLM
  from .linear import W8A8B8O8Linear, W8A8BFP32O32LinearSiLU, W8A8BFP32OFP32Linear
label_desc: Use image node_id 4 for calling the LLamaSmoothquantAttention obj's underlying member method code with example usage: obj._shape(tensor, seq_len, bsz) and returns: tensor