docstr: Convert scores into flat numpy arrays.
docstr_tok: ["Convert","scores","into","flat","numpy","arrays","."]
code: def _process_torch_scores(self, scores):
      """
      Convert scores into flat numpy arrays.
      """
      if constants.Device.CUDA in scores.device.type:
          scores = scores.cpu()
      return scores.numpy().ravel()
  
imports: import time
  import numpy
  import pandas
  from sklearn.base import BaseEstimator
  from sklearn.feature_selection import SelectorMixin
  from sklearn.utils.validation import check_is_fitted
  import torch
  from nni.feature_engineering.feature_selector import FeatureSelector
  from .None import constants
  from .fginitialize import PrepareData
  from .fgtrain import _train
label_desc: Use image node_id 8 for calling the FeatureGradientSelector obj's underlying member method code with example usage: obj._process_torch_scores(scores) and returns: scores