code: def forward(self, originals, generateds, images=None):
      if images is not None:
          # In case they've been pre-computed.
          histograms = []
          for image in images:
              _, histogram = self.compute_histogram(image=image)
              histograms.append(histogram)
      else:
          images, histograms = [], []
          for original, generated in zip(originals, generateds):
              image, histogram = self.compute_histogram(
                  original=original, generated=generated
              )
              images.append(image)
              histograms.append(histogram)
  
      N = len(histograms)
      histograms = torch.FloatTensor(histograms).to(self.device)
  
      non_zeros = (torch.sum(histograms, dim=-1) != 0.0).long()
      seq_lengths = non_zeros.sum(dim=-1).tolist()
  
      mlp_outs = self.mlp(histograms).reshape(N, self.n_rows)
      features = []
  
      for mlp_out, seq_length in zip(mlp_outs, seq_lengths):
          if seq_length > 0:
              Rs = mlp_out[:seq_length]
              if self.agg == "mean":
                  features.append(
                      torch.cat(
                          [
                              torch.mean(Rs).unsqueeze(0),
                              torch.mean(Rs).unsqueeze(0),
                              torch.mean(Rs).unsqueeze(0),
                          ]
                      ).unsqueeze(0)
                  )
              elif self.agg == "min":
                  features.append(
                      torch.cat(
                          [
                              torch.min(Rs).unsqueeze(0),
                              torch.min(Rs).unsqueeze(0),
                              torch.min(Rs).unsqueeze(0),
                          ]
                      ).unsqueeze(0)
                  )
              elif self.agg == "max":
                  features.append(
                      torch.cat(
                          [
                              torch.max(Rs).unsqueeze(0),
                              torch.max(Rs).unsqueeze(0),
                              torch.max(Rs).unsqueeze(0),
                          ]
                      ).unsqueeze(0)
                  )
              elif self.agg == "all":
                  features.append(
                      torch.cat(
                          [
                              torch.min(Rs).unsqueeze(0),
                              torch.mean(Rs).unsqueeze(0),
                              torch.max(Rs).unsqueeze(0),
                          ]
                      ).unsqueeze(0)
                  )
          else:
              features.append(
                  torch.FloatTensor([0.0, 0.0, 0.0]).unsqueeze(0)
              )  # .cuda()
      features = torch.cat(features)
      logits = self.layer_final(features)
      histograms_out = [
          histogram.cpu().numpy() for histogram in histograms
      ]
      return logits, histograms_out, images
  
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 4 for calling the SummaCConv obj's underlying member method code with example usage: obj.forward(originals, generateds, images) and returns: logits,  histograms_out,  images