code: def __init__(
      self,
      d_model,
      nhead,
      dropout=0.0,
      activation="relu",
      normalize_before=False,
  ):
      super().__init__()
      self.multihead_attn = nn.MultiheadAttention(
          d_model, nhead, dropout=dropout
      )
  
      self.norm = nn.LayerNorm(d_model)
      self.dropout = nn.Dropout(dropout)
  
      self.activation = _get_activation_fn(activation)
      self.normalize_before = normalize_before
  
      self._reset_parameters()
  
imports: import copy
  import math
  from typing import Optional
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
label_desc: Use image node_id 1 to create a new CrossAttentionLayer object from inherited base classes: nn with example: obj = CrossAttentionLayer(d_model, nhead, dropout, activation, normalize_before)