code: def __init__(
      self,
      *,
      layer: Union[
          Type[nn.MultiheadAttention], Type[DPMultiheadAttention]
      ],
      batch_size: int,
      source_seq_len: int,
      targ_seq_len: int,
      embed_dim: int,
      num_heads: int,
      dropout: float = 0.0,
      bias: bool = True,
      add_bias_kv: bool = False,
      add_zero_attn: bool = False,
      kdim: Optional[int] = None,
      vdim: Optional[int] = None,
      batch_first: bool = False,
      random_seed: Optional[int] = None,
      criterion: Callable = F.cross_entropy,
  ) -> None:
      super().__init__(random_seed=random_seed, criterion=criterion)
  
      kdim = kdim if kdim else embed_dim
      vdim = vdim if vdim else embed_dim
  
      self._input_tensor = (
          torch.randn(targ_seq_len, batch_size, embed_dim)
          if not batch_first
          else torch.randn(batch_size, targ_seq_len, embed_dim)
      )
      self._key = (
          torch.randn(source_seq_len, batch_size, kdim)
          if not batch_first
          else torch.randn(batch_size, source_seq_len, kdim)
      )
      self._value = (
          torch.randn(source_seq_len, batch_size, vdim)
          if not batch_first
          else torch.randn(batch_size, source_seq_len, vdim)
      )
      self._module = layer(
          embed_dim,
          num_heads,
          dropout=dropout,
          bias=bias,
          add_bias_kv=add_bias_kv,
          add_zero_attn=add_zero_attn,
          kdim=kdim,
          vdim=vdim,
      )
      self._labels = (
          torch.randn(targ_seq_len, batch_size, embed_dim)
          if not batch_first
          else torch.randn(batch_size, targ_seq_len, embed_dim)
      )
  
imports: from enum import Enum
  from typing import Callable, Dict, Optional, Tuple, Type, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from opacus.grad_sample import GradSampleModule
  from opacus.grad_sample.utils import wrap_model
  from opacus.layers import DPGRU, DPLSTM, DPRNN, DPMultiheadAttention
  from opacus.layers.dp_rnn import DPRNNBase
label_desc: Use image node_id 1 to create a new MHABase object from inherited base classes: Layer with example: obj = MHABase()