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: Append all gradients to a pickle file.
  
  Args:
      epoch: Current epoch
  Returns:
      Nothing!
docstr_tok: ["Append","all","gradients","to","a","pickle","file",".","Args",":","epoch",":","Current","epoch","Returns",":","Nothing","!"]
code: def dump(self, epoch):
      """
      Append all gradients to a pickle file.
  
      Args:
          epoch: Current epoch
      Returns:
          Nothing!
      """
      with open(
          self.pars.save_path + "/grad_dict_{}.pkl".format(self.name),
          "ab",
      ) as f:
          pkl.dump([self.saver], f)
      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 3 for calling the GradientMeasure obj's underlying member method code with example usage: obj.dump(epoch) without return types