code: def init_like_torch(link):
      # Mimic torch's default parameter initialization
      # TODO(muupan): Use chainer's initializers when it is merged
      for li in link.links():
          if isinstance(li, L.Linear):
              out_channels, in_channels = li.W.shape
              stdv = 1 / np.sqrt(in_channels)
              li.W.array[:] = np.random.uniform(
                  -stdv, stdv, size=li.W.shape
              )
              if li.b is not None:
                  li.b.array[:] = np.random.uniform(
                      -stdv, stdv, size=li.b.shape
                  )
          elif isinstance(li, L.Convolution2D):
              out_channels, in_channels, kh, kw = li.W.shape
              stdv = 1 / np.sqrt(in_channels * kh * kw)
              li.W.array[:] = np.random.uniform(
                  -stdv, stdv, size=li.W.shape
              )
              if li.b is not None:
                  li.b.array[:] = np.random.uniform(
                      -stdv, stdv, size=li.b.shape
                  )
  
label_desc: Use image node_id 1 for calling a global function with example usage: init_like_torch(link) without return types