class_docstr: RWKV module.
  
  Args:
      size: Input/Output size.
      linear_size: Feed-forward hidden size.
      attention_size: SelfAttention hidden size.
      context_size: Context size for WKV computation.
      block_id: Block index.
      num_blocks: Number of blocks in the architecture.
      normalization_class: Normalization layer class.
      normalization_args: Normalization layer arguments.
      att_dropout_rate: Dropout rate for the attention module.
      ffn_dropout_rate: Dropout rate for the feed-forward module.
docstr: Compute receptance weighted key value.
  
  Args:
      x: RWKV input sequences. (B, L, size)
      state: Decoder hidden states. [5 x (B, D_att/size, N)]
  
  Returns:
      x: RWKV output sequences. (B, L, size)
      x: Decoder hidden states. [5 x (B, D_att/size, N)]
docstr_tok: ["Compute","receptance","weighted","key","value",".","Args",":","x",":","RWKV","input","sequences",".","(","B",",","L",",","size",")","state",":","Decoder","hidden","states",".","[","5","x","(","B",",","D_att\/size",",","N",")","]","Returns",":","x",":","RWKV","output","sequences",".","(","B",",","L",",","size",")","x",":","Decoder","hidden","states",".","[","5","x","(","B",",","D_att\/size",",","N",")","]"]
code: def forward(
      self,
      x: torch.Tensor,
      state: Optional[torch.Tensor] = None,
  ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
      """Compute receptance weighted key value.
  
      Args:
          x: RWKV input sequences. (B, L, size)
          state: Decoder hidden states. [5 x (B, D_att/size, N)]
  
      Returns:
          x: RWKV output sequences. (B, L, size)
          x: Decoder hidden states. [5 x (B, D_att/size, N)]
  
      """
      att, state = self.att(self.layer_norm_att(x), state=state)
      x = x + self.dropout_att(att)
  
      ffn, state = self.ffn(self.layer_norm_ffn(x), state=state)
      x = x + self.dropout_ffn(ffn)
  
      return x, state
  
imports: from typing import Dict, Optional, Tuple
  import torch
  from espnet2.asr_transducer.decoder.modules.rwkv.attention import SelfAttention
  from espnet2.asr_transducer.decoder.modules.rwkv.feed_forward import FeedForward
label_desc: Use image node_id 2 for calling the RWKV obj's underlying member method code with example usage: obj.forward(x, state) and returns: x,  state