code: def test_sparsify_and_densify():
      """Test that sparsify and densify work as inverses."""
      # Test on identity matrix
      num_samples = 10
      num_features = num_samples
      X = np.eye(num_samples)
      X_sparse = dc.data.sparsify_features(X)
      X_reconstructed = dc.data.densify_features(X_sparse, num_features)
      np.testing.assert_array_equal(X, X_reconstructed)
  
      # Generate random sparse features dataset
      np.random.seed(123)
      p = 0.05
      X = np.random.binomial(1, p, size=(num_samples, num_features))
      X_sparse = dc.data.sparsify_features(X)
      X_reconstructed = dc.data.densify_features(X_sparse, num_features)
      np.testing.assert_array_equal(X, X_reconstructed)
  
      # Test edge case with array of all zeros
      X = np.zeros((num_samples, num_features))
      X_sparse = dc.data.sparsify_features(X)
      X_reconstructed = dc.data.densify_features(X_sparse, num_features)
      np.testing.assert_array_equal(X, X_reconstructed)
  
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 4 for calling a global function with example usage: test_sparsify_and_densify() without return types