code: def file_regression(input_file, model_name, model_type):
      # Load classifier:
  
      if not os.path.isfile(input_file):
          print("fileClassification: wav file not found!")
          return -1, -1, -1
  
      regression_models = glob.glob(model_name + "_*")
      regression_models2 = []
      for r in regression_models:
          if r[-5::] != "MEANS":
              regression_models2.append(r)
      regression_models = regression_models2
      regression_names = []
      for r in regression_models:
          regression_names.append(r[r.rfind("_") + 1 : :])
  
      # FEATURE EXTRACTION
      # LOAD ONLY THE FIRST MODEL (for mt_win, etc)
      if (
          model_type == "svm"
          or model_type == "svm_rbf"
          or model_type == "randomforest"
      ):
          (
              _,
              _,
              _,
              mid_window,
              mid_step,
              short_window,
              short_step,
              compute_beat,
          ) = load_model(regression_models[0], True)
  
      # read audio file and convert to mono
      samping_rate, signal = audioBasicIO.read_audio_file(input_file)
      signal = audioBasicIO.stereo_to_mono(signal)
      # feature extraction:
      mid_features, s, _ = aF.mid_feature_extraction(
          signal,
          samping_rate,
          mid_window * samping_rate,
          mid_step * samping_rate,
          round(samping_rate * short_window),
          round(samping_rate * short_step),
      )
      # long term averaging of mid-term statistics
      mid_features = mid_features.mean(axis=1)
      if compute_beat:
          beat, beat_conf = aF.beat_extraction(s, short_step)
          mid_features = np.append(mid_features, beat)
          mid_features = np.append(mid_features, beat_conf)
  
      # REGRESSION
      R = []
      for ir, r in enumerate(regression_models):
          if not os.path.isfile(r):
              print("fileClassification: input model_name not found!")
              return (-1, -1, -1)
          if (
              model_type == "svm"
              or model_type == "svm_rbf"
              or model_type == "randomforest"
          ):
              model, mean, std, _, _, _, _, _ = load_model(r, True)
          curFV = (mid_features - mean) / std  # normalization
          # classification
          R.append(regression_wrapper(model, model_type, curFV))
      return R, regression_names
  
imports: from __future__ import print_function
  from imblearn.under_sampling import RandomUnderSampler
  from imblearn.over_sampling import SMOTE
  from sklearn.model_selection import GroupShuffleSplit
  from pyAudioAnalysis import audioBasicIO
  from pyAudioAnalysis import MidTermFeatures
  import sys
  import numpy
  import os
  import glob
  import pickle
  import csv
  import ntpath
  from scipy import linalg
  from scipy.spatial import distance
  import sklearn.svm
  import sklearn.decomposition
  import sklearn.ensemble
  import plotly
  import plotly.subplots
  import plotly.graph_objs
  import sklearn.metrics
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 24 for calling a global function with example usage: file_regression(input_file, model_name, model_type) and returns: R,  regression_names, unknown,  unknown,  unknown, unknown,  unknown,  unknown