code: def compute_histogram(
      self, original=None, generated=None, image=None
  ):
      # Takes the two texts, and generates a (n_rows, 2*n_bins)
  
      if image is None:
          image = self.build_image(original, generated)
  
      N_depth, N_ori, N_gen = image.shape
  
      full_histogram = []
      for i_gen in range(N_gen):
          histos = []
  
          for i_depth in range(N_depth):
              if (
                  (i_depth % 3 == 0 and "e" in self.nli_labels)
                  or (i_depth % 3 == 1 and "c" in self.nli_labels)
                  or (i_depth % 3 == 2 and "n" in self.nli_labels)
              ):
                  histo, X = np.histogram(
                      image[i_depth, :, i_gen],
                      range=(0, 1),
                      bins=self.bins,
                      density=self.norm_histo,
                  )
                  histos.append(histo)
  
          if self.norm_histo:
              histos = [[N_ori, N_gen]] + histos
          histogram_row = np.concatenate(histos)
          full_histogram.append(histogram_row)
  
      n_rows_missing = self.n_rows - len(full_histogram)
      full_histogram += [[0.0] * self.full_size] * n_rows_missing
      full_histogram = full_histogram[: self.n_rows]
      full_histogram = np.array(full_histogram)
      return image, full_histogram
  
imports: from transformers import AutoTokenizer, AutoModelForSequenceClassification
  import nltk
  import numpy
  import torch
  import os
  import json
  from .None import utils_misc
label_desc: Use image node_id 3 for calling the SummaCConv obj's underlying member method code with example usage: obj.compute_histogram(original, generated, image) and returns: image,  full_histogram