code: def feature_transform_for(args, n_fft):
      return FeatureTransform(
          # Mel options,
          fs=args.fbank_fs,
          n_fft=n_fft,
          n_mels=args.n_mels,
          fmin=args.fbank_fmin,
          fmax=args.fbank_fmax,
          # Normalization
          stats_file=args.stats_file,
          apply_uttmvn=args.apply_uttmvn,
          uttmvn_norm_means=args.uttmvn_norm_means,
          uttmvn_norm_vars=args.uttmvn_norm_vars,
      )
  
imports: from typing import List, Tuple, Union
  import librosa
  import numpy
  import torch
  from torch_complex.tensor import ComplexTensor
  from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
label_desc: Use image node_id 2 for calling a global function with example usage: feature_transform_for(args, n_fft) and returns: FeatureTransform