code: def test_lecun_init_torch():
      nseed = args.seed
      random.seed(nseed)
      torch.manual_seed(nseed)
      numpy.random.seed(nseed)
      os.environ["CHAINER_SEED"] = str(nseed)
      import espnet.nets.pytorch_backend.e2e_asr as m
  
      model = m.E2E(40, 5, args)
      b = model.ctc.ctc_lo.bias.data.numpy()
      assert numpy.all(b == 0.0)
      w = model.ctc.ctc_lo.weight.data.numpy()
      numpy.testing.assert_allclose(w.mean(), 0.0, 1e-2, 1e-2)
      numpy.testing.assert_allclose(
          w.var(), 1.0 / w.shape[1], 1e-2, 1e-2
      )
  
      for name, p in model.named_parameters():
          print(name)
          data = p.data.numpy()
          if "embed" in name:
              numpy.testing.assert_allclose(
                  data.mean(), 0.0, 5e-2, 5e-2
              )
              numpy.testing.assert_allclose(data.var(), 1.0, 5e-2, 5e-2)
          elif "dec.decoder.0.bias_ih" in name:
              assert data.sum() == data.size // 4
          elif "dec.decoder.1.bias_ih" in name:
              assert data.sum() == data.size // 4
          elif data.ndim == 1:
              assert numpy.all(data == 0.0)
          else:
              numpy.testing.assert_allclose(
                  data.mean(), 0.0, 5e-2, 5e-2
              )
              numpy.testing.assert_allclose(
                  data.var(),
                  1.0 / numpy.prod(data.shape[1:]),
                  5e-2,
                  5e-2,
              )
  
imports: import argparse
  import os
  import random
  import numpy
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: test_lecun_init_torch() without return types