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 os
  import pdb
  import copy
  import math
  import numpy
  from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import torch
  from torch import nn
  import torch.utils.checkpoint
  import torch.nn.functional
  from torch.cuda.amp import autocast
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from transformers.models.opt.modeling_opt import OPTAttention
label_desc: Use image node_id 2 for calling the OPTAttention_Mask obj's underlying member method code with example usage: obj._shape(tensor, seq_len, bsz) and returns: tensor