code: def prepare_transformer_encoder_params(
      input_dim,
      num_heads,
      ffn_dim,
      normalize_before,
      dropout,
      attention_dropout,
      relu_dropout,
  ):
      args = argparse.Namespace()
      args.encoder_embed_dim = input_dim
      args.encoder_attention_heads = num_heads
      args.attention_dropout = attention_dropout
      args.dropout = dropout
      args.activation_dropout = relu_dropout
      args.encoder_normalize_before = normalize_before
      args.encoder_ffn_embed_dim = ffn_dim
      return args
  
imports: import argparse
  import math
  from collections.abc import Iterable
  import torch
  import torch.nn
  from examples.speech_recognition.data.data_utils import lengths_to_encoder_padding_mask
  from fairseq import utils
  from fairseq.models import FairseqEncoder, FairseqEncoderDecoderModel, FairseqEncoderModel, FairseqIncrementalDecoder, register_model, register_model_architecture
  from fairseq.modules import LinearizedConvolution, TransformerDecoderLayer, TransformerEncoderLayer, VGGBlock
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_transformer_encoder_params(input_dim, num_heads, ffn_dim, normalize_before, dropout, attention_dropout, relu_dropout) and returns: args