code: def initializer(model: torch.nn.Module, args: Namespace):
      """Initialize Transducer model.
  
      Args:
          model: Transducer model.
          args: Namespace containing model options.
  
      """
      for name, p in model.named_parameters():
          if any(
              x in name for x in ["enc.", "dec.", "transducer_tasks."]
          ):
              if p.dim() == 1:
                  # bias
                  p.data.zero_()
              elif p.dim() == 2:
                  # linear weight
                  n = p.size(1)
                  stdv = 1.0 / math.sqrt(n)
                  p.data.normal_(0, stdv)
              elif p.dim() in (3, 4):
                  # conv weight
                  n = p.size(1)
                  for k in p.size()[2:]:
                      n *= k
                      stdv = 1.0 / math.sqrt(n)
                      p.data.normal_(0, stdv)
  
      if args.dtype != "custom":
          model.dec.embed.weight.data.normal_(0, 1)
  
          for i in range(model.dec.dlayers):
              set_forget_bias_to_one(
                  getattr(model.dec.decoder[i], "bias_ih_l0")
              )
              set_forget_bias_to_one(
                  getattr(model.dec.decoder[i], "bias_hh_l0")
              )
  
imports: import math
  from argparse import Namespace
  import torch
  from espnet.nets.pytorch_backend.initialization import set_forget_bias_to_one
label_desc: Use image node_id 1 for calling a global function with example usage: initializer(model, args) without return types