code: def test_pad_features():
      """Test that pad_features pads features correctly."""
      batch_size = 100
      num_features = 10
  
      # Test cases where n_samples < 2*n_samples < batch_size
      n_samples = 29
      X_b = np.zeros((n_samples, num_features))
  
      X_out = dc.data.pad_features(batch_size, X_b)
      assert len(X_out) == batch_size
  
      # Test cases where n_samples < batch_size
      n_samples = 79
      X_b = np.zeros((n_samples, num_features))
      X_out = dc.data.pad_features(batch_size, X_b)
      assert len(X_out) == batch_size
  
      # Test case where n_samples == batch_size
      n_samples = 100
      X_b = np.zeros((n_samples, num_features))
      X_out = dc.data.pad_features(batch_size, X_b)
      assert len(X_out) == batch_size
  
      # Test case for object featurization.
      n_samples = 2
      X_b = np.array([{"a": 1}, {"b": 2}])
      X_out = dc.data.pad_features(batch_size, X_b)
      assert len(X_out) == batch_size
  
      # Test case for more complicated object featurization
      n_samples = 2
      X_b = np.array([(1, {"a": 1}), (2, {"b": 2})])
      X_out = dc.data.pad_features(batch_size, X_b)
      assert len(X_out) == batch_size
  
      # Test case with multidimensional data
      n_samples = 50
      num_atoms = 15
      d = 3
      X_b = np.zeros((n_samples, num_atoms, d))
      X_out = dc.data.pad_features(batch_size, X_b)
      assert len(X_out) == batch_size
  
imports: import random
  import math
  import unittest
  import os
  import numpy
  import pytest
  import deepchem
  import pandas
  import tempfile
label_desc: Use image node_id 5 for calling a global function with example usage: test_pad_features() without return types