code: def __init__(self, config, layer_id=0):
      super().__init__()
      self.config = config
      self.layer_id = layer_id
      hidden_size = config.hidden_size
      intermediate_size = (
          config.intermediate_size
          if config.intermediate_size is not None
          else 4 * config.hidden_size
      )
  
      self.time_shift = nn.ZeroPad2d((0, 0, 1, -1))
      self.time_mix_key = nn.Parameter(torch.empty(1, 1, hidden_size))
      self.time_mix_receptance = nn.Parameter(
          torch.empty(1, 1, hidden_size)
      )
  
      self.key = nn.Linear(hidden_size, intermediate_size, bias=False)
      self.receptance = nn.Linear(hidden_size, hidden_size, bias=False)
      self.value = nn.Linear(intermediate_size, hidden_size, bias=False)
  
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 1 to create a new RwkvFeedForward object from inherited base classes: nn with example: obj = RwkvFeedForward(config, layer_id)