code: def forward(
      self,
      hidden_states: Optional[Tuple[torch.FloatTensor]],
      layer_past: Optional[Tuple[torch.Tensor]] = None,
      use_cache: Optional[bool] = False,
      output_attentions: Optional[bool] = False,
  ):
      residual = hidden_states
      hidden_states = self.ln1(hidden_states)
  
      attn_outputs = self.attn(
          hidden_states,
          layer_past=layer_past,
          use_cache=use_cache,
          output_attentions=output_attentions,
      )
      attn_output = attn_outputs[0]
      outputs = attn_outputs[1:]
      hidden_states = attn_output + residual
  
      residual = hidden_states
      hidden_states = self.ln2(hidden_states)
      hidden_states = self.l1(hidden_states)
      hidden_states = self.act(hidden_states)
      hidden_states = self.l2(hidden_states)
      hidden_states = residual + self.drop(hidden_states)
  
      if use_cache:
          outputs = (hidden_states,) + outputs
      else:
          outputs = (hidden_states,) + outputs[1:]
  
      return outputs
  
imports: import math
  import os
  from dataclasses import dataclass
  from typing import Optional, Tuple
  import numpy
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import functional
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_trajectory_transformer import TrajectoryTransformerConfig
label_desc: Use image node_id 2 for calling the Block obj's underlying member method code with example usage: obj.forward(hidden_states, layer_past, use_cache, output_attentions) and returns: outputs