code: def forward(
      self, hidden, state=None, use_cache=False, output_attentions=False
  ):
      if self.layer_id == 0:
          hidden = self.pre_ln(hidden)
  
      attention, state = self.attention(
          self.ln1(hidden), state=state, use_cache=use_cache
      )
      hidden = hidden + attention
  
      feed_forward, state = self.feed_forward(
          self.ln2(hidden), state=state
      )
      hidden = hidden + feed_forward
  
      outputs = (hidden, state)
      if output_attentions:
          outputs += (attention,)
      else:
          outputs += (None,)
  
      return outputs
  
imports: import math
  from dataclasses import dataclass
  from pathlib import Path
  from typing import List, Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import CrossEntropyLoss
  from ...modeling_utils import PreTrainedModel
  from ...utils import ModelOutput, add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, is_bitsandbytes_available, is_ninja_available, is_torch_cuda_available, logging
  from .configuration_rwkv import RwkvConfig
label_desc: Use image node_id 2 for calling the RwkvBlock obj's underlying member method code with example usage: obj.forward(hidden, state, use_cache, output_attentions) and returns: outputs