code: def init_torch_weight_random(m, rand_range):
      for name, p in m.named_parameters():
          p.data.uniform_(rand_range[0], rand_range[1])
          # set small bias for <blank> output
          if "wordlm.lo.bias" in name or "dec.output.bias" in name:
              p.data[0] = -10.0
  
file_path: espnet/test/test_recog.py
imports: import argparse
  import numpy
  import pytest
  import torch
  import espnet.lm.pytorch_backend.extlm
  import espnet.nets.pytorch_backend.lm.default
  from espnet.nets.pytorch_backend import e2e_asr
label_desc: Use image node_id 3 for calling a global function with example usage: init_torch_weight_random(m, rand_range) without return types