class_docstr: Default RNNLM for `LMInterface` Implementation.
  
  Note:
      PyTorch seems to have memory leak when one GPU compute this after data parallel.
      If parallel GPUs compute this, it seems to be fine.
      See also https://github.com/espnet/espnet/issues/1075
class_docstr_tok: ["Default","RNNLM","for","`","LMInterface","`","Implementation",".","Note",":","PyTorch","seems","to","have","memory","leak","when","one","GPU","compute","this","after","data","parallel",".","If","parallel","GPUs","compute","this",",","it","seems","to","be","fine",".","See","also","https",":","\/\/github.com\/espnet\/espnet\/issues\/1075"]
docstr: Load state dict.
docstr_tok: ["Load","state","dict","."]
code: def load_state_dict(self, d):
      """Load state dict."""
      self.model.load_state_dict(d)
  
imports: import logging
  from typing import Any, List, Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from espnet.nets.lm_interface import LMInterface
  from espnet.nets.pytorch_backend.e2e_asr import to_device
  from espnet.nets.scorer_interface import BatchScorerInterface
  from espnet.utils.cli_utils import strtobool
label_desc: Use image node_id 4 for calling the DefaultRNNLM obj's underlying member method code with example usage: obj.load_state_dict(d) without return types