code: def test_merge():
      """Test that dataset merge works."""
      num_datapoints = 10
      num_features = 10
      num_tasks = 1
      num_datasets = 4
      datasets = []
      for i in range(num_datasets):
          Xi = np.random.rand(num_datapoints, num_features)
          yi = np.random.randint(2, size=(num_datapoints, num_tasks))
          wi = np.ones((num_datapoints, num_tasks))
          idsi = np.array(["id"] * num_datapoints)
          dataseti = dc.data.DiskDataset.from_numpy(Xi, yi, wi, idsi)
          datasets.append(dataseti)
  
      new_data = dc.data.datasets.DiskDataset.merge(datasets)
      x = [1, 2, 3]
      X = np.array(x)
      y = np.array(x)
      a = dc.data.NumpyDataset(X, y)
      b = dc.data.NumpyDataset(X, y)
      c = dc.data.NumpyDataset.merge([a, b])
      assert c.y.shape == (
          6,
          1,
      )  # test to check if merge works when y is one dimensional
      # Check that we have all the data in
      assert new_data.X.shape == (
          num_datapoints * num_datasets,
          num_features,
      )
      assert new_data.y.shape == (
          num_datapoints * num_datasets,
          num_tasks,
      )
      assert len(new_data.tasks) == len(datasets[0].tasks)
  
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 23 for calling a global function with example usage: test_merge() without return types