class_docstr: 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 random
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from transformers.activations import ACT2FN
  from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
  from transformers.modeling_utils import PreTrainedModel
  from transformers import OPTConfig
  from text_generation_server.utils.layers import FastLinear, TensorParallelColumnLinear, TensorParallelEmbedding, TensorParallelRowLinear, TensorParallelHead
label_desc: Use image node_id 2 for calling the OPTAttention obj's underlying member method code with example usage: obj._shape(tensor, seq_len, bsz) and returns: tensor