class_docstr: Transformer Encoder Block
class_docstr_tok: ["Transformer","Encoder","Block"]
code: def forward(
      self, x: torch.Tensor, mask: Optional[torch.Tensor] = None
  ) -> torch.Tensor:
      output = x
  
      for i in range(self.num_layers):
          normed_output = self.layer_norm_input(output)
          output = output + self.dropout(
              self.attention[i](
                  normed_output, normed_output, normed_output, mask
              )
          )
          normed_output = self.layer_norm_attention(output)
          output = output + self.dropout(
              self.position_feed_forward[i](normed_output)
          )
  
      # (batch_size, seq_len, d_model)
      return self.layer_norm_output(output)
  
imports: import math
  from typing import Any, Callable, Optional, Tuple
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the EncoderBlock obj's underlying member method code with example usage: obj.forward(x, mask) and returns: self