code: def __init__(
      self,
      embed_dim: int = 768,
      depth: int = 12,
      num_heads: int = 12,
      dropout_rate: float = 0.0,
      dropout_attn: float = 0.0,
  ) -> None:
      super().__init__()
      self.blocks = nn.Sequential(
          *(
              TransformerEncoderBlock(
                  embed_dim, num_heads, dropout_rate, dropout_attn
              )
              for _ in range(depth)
          )
      )
      self.results: List[torch.Tensor] = []
  
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 TransformerEncoder object from inherited base classes: Module with example: obj = TransformerEncoder(embed_dim, depth, num_heads, dropout_rate, dropout_attn)