code: def get_float_tensor(features_list, batch_size):
      if features_list is None or features_list[0] is None:
          return None
      seq_len = max(len(x) for x in features_list)
      feature_len = len(features_list[0][0])
      features = torch.FloatTensor(
          batch_size, seq_len, feature_len
      ).zero_()
      for i, f in enumerate(features_list):
          features[i, : len(f), :] = torch.FloatTensor(f)
      return features
  
file_path: stanza/stanza/models/common/data.py
imports: import logging
  import random
  import torch
  import stanza.models.common.seq2seq_constant
  from stanza.models.common.doc import HEAD, ID, UPOS
label_desc: Use image node_id 3 for calling a global function with example usage: get_float_tensor(features_list, batch_size) and returns: features, None