class_docstr: This is DP-friendly implementation of nn.MultiheadAttention.
  For full reference see original module refer to
  :class:`torch.nn.MultiheadAttention`.
  
  Current implementation leverages pytorch modules as building blocks
  to allow DP engine to calculate per-sample gradients.
  This is in contrast with original implementation based on nn.functional.
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code: def unsqueeze_0_2(self, t):
      return torch.unsqueeze(torch.unsqueeze(t, 0), 0)
  
file_path: opacus/opacus/layers/dp_multihead_attention.py
imports: import warnings
  from collections import OrderedDict
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 4 for calling the DPMultiheadAttention obj's underlying member method code with example usage: obj.unsqueeze_0_2(t) and returns: torch