code: def init_hid_cache(self, batch_sz):
      hid = []
      for layer in self.model.layers:
          param = next(self.model.parameters())
          h = torch.zeros(
              batch_sz,
              layer.get_cache_size(),
              self.config.d_model,
              dtype=param.dtype,
              device=param.device,
          )
          hid.append(h)
      return hid
  
imports: import logging
  from dataclasses import dataclass
  from typing import Dict, List, Optional
  import torch
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import FairseqIncrementalDecoder, FairseqLanguageModel, register_model
  from .adaptive_span_model import TransformerSeq
label_desc: Use image node_id 4 for calling the AdaptiveSpanDecoder obj's underlying member method code with example usage: obj.init_hid_cache(batch_sz) and returns: hid