code: def test_pad_batches():
      """Test that pad_batch pads batches correctly."""
      batch_size = 100
      num_features = 10
      num_tasks = 5
  
      # Test cases where n_samples < 2*n_samples < batch_size
      n_samples = 29
      X_b = np.zeros((n_samples, num_features))
      y_b = np.zeros((n_samples, num_tasks))
      w_b = np.zeros((n_samples, num_tasks))
      ids_b = np.zeros((n_samples,))
  
      X_out, y_out, w_out, ids_out = dc.data.pad_batch(
          batch_size, X_b, y_b, w_b, ids_b
      )
      assert (
          len(X_out)
          == len(y_out)
          == len(w_out)
          == len(ids_out)
          == batch_size
      )
  
      # Test cases where n_samples < batch_size
      n_samples = 79
      X_b = np.zeros((n_samples, num_features))
      y_b = np.zeros((n_samples, num_tasks))
      w_b = np.zeros((n_samples, num_tasks))
      ids_b = np.zeros((n_samples,))
  
      X_out, y_out, w_out, ids_out = dc.data.pad_batch(
          batch_size, X_b, y_b, w_b, ids_b
      )
      assert (
          len(X_out)
          == len(y_out)
          == len(w_out)
          == len(ids_out)
          == batch_size
      )
  
      # Test case where n_samples == batch_size
      n_samples = 100
      X_b = np.zeros((n_samples, num_features))
      y_b = np.zeros((n_samples, num_tasks))
      w_b = np.zeros((n_samples, num_tasks))
      ids_b = np.zeros((n_samples,))
  
      X_out, y_out, w_out, ids_out = dc.data.pad_batch(
          batch_size, X_b, y_b, w_b, ids_b
      )
      assert (
          len(X_out)
          == len(y_out)
          == len(w_out)
          == len(ids_out)
          == batch_size
      )
  
      # Test case for object featurization.
      n_samples = 2
      X_b = np.array([{"a": 1}, {"b": 2}])
      y_b = np.zeros((n_samples, num_tasks))
      w_b = np.zeros((n_samples, num_tasks))
      ids_b = np.zeros((n_samples,))
      X_out, y_out, w_out, ids_out = dc.data.pad_batch(
          batch_size, X_b, y_b, w_b, ids_b
      )
      assert (
          len(X_out)
          == len(y_out)
          == len(w_out)
          == len(ids_out)
          == batch_size
      )
  
      # Test case for more complicated object featurization
      n_samples = 2
      X_b = np.array([(1, {"a": 1}), (2, {"b": 2})])
      y_b = np.zeros((n_samples, num_tasks))
      w_b = np.zeros((n_samples, num_tasks))
      ids_b = np.zeros((n_samples,))
      X_out, y_out, w_out, ids_out = dc.data.pad_batch(
          batch_size, X_b, y_b, w_b, ids_b
      )
      assert (
          len(X_out)
          == len(y_out)
          == len(w_out)
          == len(ids_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))
      y_b = np.zeros((n_samples, num_tasks))
      w_b = np.zeros((n_samples, num_tasks))
      ids_b = np.zeros((n_samples,))
  
      X_out, y_out, w_out, ids_out = dc.data.pad_batch(
          batch_size, X_b, y_b, w_b, ids_b
      )
      assert (
          len(X_out)
          == len(y_out)
          == len(w_out)
          == len(ids_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 6 for calling a global function with example usage: test_pad_batches() without return types