code: def forward(
      self,
      x,
      mask,
      layer_past=None,
      attention_mask=None,
      head_mask=None,
      use_cache=False,
      output_attentions=False,
  ):
      normed = self.layernorm1(x)
      attn_outputs = self.multi_head_attention(
          normed,
          normed,
          normed,
          mask,
          layer_past=layer_past,
          attention_mask=attention_mask,
          head_mask=head_mask,
          use_cache=use_cache,
          output_attentions=output_attentions,
      )
      attn_output = attn_outputs[0]
      attn_output = self.dropout1(attn_output)
      out1 = x + attn_output
  
      out2 = self.layernorm2(out1)
      ffn_output = self.ffn(out2)
      ffn_output = self.dropout2(ffn_output)
      out2 = out1 + ffn_output
  
      outputs = (out2,) + attn_outputs[1:]
      return outputs
  
imports: from typing import Optional, Tuple, Union
  import numpy
  import torch
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutput
  from ...modeling_utils import PreTrainedModel
  from ...pytorch_utils import Conv1D, find_pruneable_heads_and_indices, prune_linear_layer
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_ctrl import CTRLConfig
label_desc: Use image node_id 2 for calling the EncoderLayer obj's underlying member method code with example usage: obj.forward(x, mask, layer_past, attention_mask, head_mask, use_cache, output_attentions) and returns: outputs