code: def get_negative_expectation(q_samples, average=True):
      """Computes the negative part of a JS Divergence.
      Args:
          q_samples: Negative samples.
          average: Average the result over samples.
      Returns:
          th.Tensor
      """
      log_2 = math.log(2.0)
      Eq = F.softplus(-q_samples) + q_samples - log_2
  
      if average:
          return Eq.mean()
      else:
          return Eq
  
file_path: dgl/examples/pytorch/mvgrl/graph/utils.py
imports: import math
  import numpy
  import torch
  import torch.nn.functional
  from sklearn.metrics import accuracy_score
  from sklearn.model_selection import GridSearchCV, StratifiedKFold
  from sklearn.svm import LinearSVC
label_desc: Use image node_id 3 for calling a global function with example usage: get_negative_expectation(q_samples, average) and returns: Eq, Eq