class_docstr: Container for gradient measure functionalities.
  Measure the gradients coming from the embedding layer to the final conv. layer
  to examine learning signal.
class_docstr_tok: ["Container","for","gradient","measure","functionalities",".","Measure","the","gradients","coming","from","the","embedding","layer","to","the","final","conv",".","layer","to","examine","learning","signal","."]
docstr: Args:
      opt:   argparse.Namespace, contains all training-specific parameters.
      name:  Name of class instance. Important for the savename.
  Returns:
      Nothing!
docstr_tok: ["Args",":","opt",":","argparse.Namespace",",","contains","all","training-specific","parameters",".","name",":","Name","of","class","instance",".","Important","for","the","savename",".","Returns",":","Nothing","!"]
code: def __init__(self, opt, name="class-it"):
      """
      Args:
          opt:   argparse.Namespace, contains all training-specific parameters.
          name:  Name of class instance. Important for the savename.
      Returns:
          Nothing!
      """
      self.pars = opt
      self.name = name
      self.saver = {
          "grad_normal_mean": [],
          "grad_normal_std": [],
          "grad_abs_mean": [],
          "grad_abs_std": [],
      }
  
imports: import warnings
  import csv
  import pickle
  import time
  import auxiliaries
  import matplotlib.pyplot
  import numpy
  import torch
  import torch.multiprocessing
  import torch.nn
  from scipy.spatial import distance
  from sklearn.preprocessing import normalize
  from tqdm import tqdm
label_desc: Use image node_id 1 to create a new GradientMeasure object  with example: obj = GradientMeasure(opt, name)