class_docstr: Multi-headed attention from 'Attention Is All You Need' paper
class_docstr_tok: ["Multi-headed","attention","from","'Attention","Is","All","You","Need","'","paper"]
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
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.nn.functional
  import torch.utils.checkpoint
  from torch import nn
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers.models.llama.modeling_llama import LlamaConfig, LlamaRMSNorm
  from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from colossalai.kernel.cuda_native.mha.flash_attn_2 import HAS_FLASH_ATTN
  from colossalai.kernel.triton.llama_act_combine_kernel import HAS_TRITON
  from colossalai.moe.layers import SparseMLP
  from colossalai.moe.manager import MOE_MANAGER
  from colossalai.moe.utils import get_activation, set_moe_args
label_desc: Use image node_id 2 for calling the OpenMoeAttention obj's underlying member method code with example usage: obj._shape(tensor, seq_len, bsz) and returns: tensor