parameters: (x_tokens)
return_statement: return int(avg_tokens + 2 * np.std(num_tokens))
docstring: Feature length is used for padding/ truncating (ensuring
      that each sequence is of the same length in batch).
      Max tokens set to avg + 2 std dev.
  
      Args:
          x_tokens: List of sequences.
  
      Returns:
          Max feature length (max tokens).
docstring_summary: Feature length is used for padding/ truncating (ensuring
      that each sequence is of the same length in batch).
      Max tokens set to avg + 2 std dev.
docstring_tokens: Feature length is used for padding / truncating ( ensuring that each sequence is of the same length in batch ) . Max tokens set to avg + 2 std dev .
function: def get_feature_length(x_tokens):
      """
      Feature length is used for padding/ truncating (ensuring
      that each sequence is of the same length in batch).
      Max tokens set to avg + 2 std dev.
  
      Args:
          x_tokens: List of sequences.
  
      Returns:
          Max feature length (max tokens).
      """
      num_tokens = np.array([len(tokens) for tokens in x_tokens])
      avg_tokens = np.mean(num_tokens)
      return int(avg_tokens + 2 * np.std(num_tokens))
function_tokens: def get_feature_length ( x_tokens ) : num_tokens = np . array ( [ len ( tokens ) for tokens in x_tokens ] ) avg_tokens = np . mean ( num_tokens ) return int ( avg_tokens + 2 * np . std ( num_tokens ) )