code: def make_transformer_args(**kwargs):
      defaults = dict(
          embed_dim=32,
          spk_embed_dim=None,
          eprenet_conv_layers=2,
          eprenet_conv_filts=5,
          eprenet_conv_chans=32,
          dprenet_layers=2,
          dprenet_units=32,
          adim=32,
          aheads=4,
          elayers=2,
          eunits=32,
          dlayers=2,
          dunits=32,
          postnet_layers=2,
          postnet_filts=5,
          postnet_chans=32,
          eprenet_dropout_rate=0.1,
          dprenet_dropout_rate=0.5,
          postnet_dropout_rate=0.1,
          transformer_enc_dropout_rate=0.1,
          transformer_enc_positional_dropout_rate=0.1,
          transformer_enc_attn_dropout_rate=0.0,
          transformer_dec_dropout_rate=0.1,
          transformer_dec_positional_dropout_rate=0.1,
          transformer_dec_attn_dropout_rate=0.3,
          transformer_enc_dec_attn_dropout_rate=0.0,
          spk_embed_integration_type="add",
          use_masking=True,
          use_weighted_masking=False,
          bce_pos_weight=1.0,
          use_batch_norm=True,
          use_scaled_pos_enc=True,
          encoder_normalize_before=True,
          decoder_normalize_before=True,
          encoder_concat_after=False,
          decoder_concat_after=False,
          transformer_init="pytorch",
          initial_encoder_alpha=1.0,
          initial_decoder_alpha=1.0,
          reduction_factor=1,
          loss_type="L1",
          use_guided_attn_loss=False,
          num_heads_applied_guided_attn=2,
          num_layers_applied_guided_attn=2,
          guided_attn_loss_sigma=0.4,
          guided_attn_loss_lambda=1.0,
          modules_applied_guided_attn=[
              "encoder",
              "decoder",
              "encoder-decoder",
          ],
      )
      defaults.update(kwargs)
      return defaults
  
file_path: espnet/test/test_e2e_tts_transformer.py
imports: from argparse import Namespace
  import numpy
  import pytest
  import torch
  from espnet.nets.pytorch_backend.e2e_tts_transformer import Transformer, subsequent_mask
  from espnet.nets.pytorch_backend.nets_utils import pad_list
label_desc: Use image node_id 1 for calling a global function with example usage: make_transformer_args() and returns: defaults