I am not sure this is a pytorch question or a python question. In

  
      github.com
  
  
    TropComplique/lda2vec-pytorch/blob/master/utils/training.py 148
import numpy as np
import torch
from torch.autograd import Variable
import torch.optim as optim
import math
from tqdm import tqdm
from torch.utils.data.dataset import Dataset
from torch.utils.data import DataLoader
from .lda2vec_loss import loss, topic_embedding


# negative sampling power
BETA = 0.75

# i add some noise to the gradient
ETA = 0.4
# i believe this helps optimization.
# the idea is taken from here:
# https://arxiv.org/abs/1511.06807
# 'Adding Gradient Noise Improves Learning for Very Deep Networks'


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line 127, why is it ok to call model(doc_indices, pivot_words, target_words) without specifying forward() method name?