code: def encode_features(X, log_degree_cutoff=4):
      X_arr = np.array(X)
      a = transform_degree(X_arr[:, 0], log_degree_cutoff)
      b = transform_transitivity(X_arr[:, 1])
      A = onehot_encoding(a, range(log_degree_cutoff + 1))
      B = onehot_encoding(b, range(11))
      return np.concatenate((A, B), axis=1)
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/dataset/twitter_tennis.py
imports: import json
  import urllib
  import numpy
  from ..signal import DynamicGraphTemporalSignal
label_desc: Use image node_id 4 for calling a global function with example usage: encode_features(X, log_degree_cutoff) and returns: np