code: def __init__(
      self,
      embed_dim: int,
      num_heads: int,
      dropout_rate: float,
      dropout_attn: float,
  ) -> None:
      super().__init__(
          ResidualAdd(
              nn.Sequential(
                  nn.LayerNorm(embed_dim),
                  MultiHeadAttention(
                      embed_dim, num_heads, dropout_attn, dropout_rate
                  ),
                  nn.Dropout(dropout_rate),
              )
          ),
          ResidualAdd(
              nn.Sequential(
                  nn.LayerNorm(embed_dim),
                  FeedForward(
                      embed_dim,
                      embed_dim,
                      embed_dim,
                      dropout_rate=dropout_rate,
                  ),
                  nn.Dropout(dropout_rate),
              )
          ),
      )
  
imports: from typing import Any, Callable, Dict, List, Optional, Tuple
  import torch
  from torch import nn
  from kornia.core import Module, Tensor
label_desc: Use image node_id 1 to create a new TransformerEncoderBlock object from inherited base classes: nn with example: obj = TransformerEncoderBlock(embed_dim, num_heads, dropout_rate, dropout_attn)