code: def test_tfa():
      from brainiak.factoranalysis.tfa import TFA
      import numpy as np
  
      n_voxel = 100
      n_tr = 20
      K = 5
      max_iter = 5
      max_num_voxel = n_voxel
      max_num_tr = n_tr
      tfa = TFA(
          K=K,
          max_iter=max_iter,
          verbose=True,
          max_num_voxel=max_num_voxel,
          max_num_tr=max_num_tr,
      )
      assert tfa, "Invalid TFA instance!"
  
      R = np.random.randint(2, high=102, size=(n_voxel, 3))
      X = [1, 2, 3]
      # Check that does NOT run with wrong data type
      with pytest.raises(TypeError) as excinfo:
          tfa.fit(X, R=R)
      assert "Input data should be an array" in str(excinfo.value)
  
      X = np.random.rand(n_voxel)
      # Check that does NOT run with wrong array dimension
      with pytest.raises(TypeError) as excinfo:
          tfa.fit(X, R=R)
      assert "Input data should be 2D array" in str(excinfo.value)
  
      X = np.random.rand(n_voxel, n_tr)
      R = [1, 2, 3]
      # Check that does NOT run with wrong data type
      with pytest.raises(TypeError) as excinfo:
          tfa.fit(X, R=R)
      assert "coordinate matrix should be an array" in str(
          excinfo.value
      )
  
      R = np.random.rand(n_voxel)
      # Check that does NOT run with wrong array dimension
      with pytest.raises(TypeError) as excinfo:
          tfa.fit(X, R=R)
      assert "coordinate matrix should be 2D array" in str(
          excinfo.value
      )
  
      R = np.random.randint(2, high=102, size=(n_voxel - 1, 3))
      # Check that does NOT run if n_voxel in X and R does not match
      with pytest.raises(TypeError) as excinfo:
          tfa.fit(X, R=R)
      assert (
          "The number of voxels should be the same in X and R"
          in str(excinfo.value)
      )
  
      R = np.random.randint(2, high=102, size=(n_voxel, 3))
      tfa.fit(X, R=R)
      assert True, "Success running TFA with one subject!"
      posterior_size = K * (tfa.n_dim + 1)
      assert (
          tfa.local_posterior_.shape[0] == posterior_size
      ), "Invalid result of TFA! (wrong # element in local_posterior)"
  
      weight_method = "ols"
      tfa = TFA(
          weight_method=weight_method,
          K=K,
          max_iter=max_iter,
          verbose=True,
          max_num_voxel=max_num_voxel,
          max_num_tr=max_num_tr,
      )
      assert tfa, "Invalid TFA instance!"
  
      X = np.random.rand(n_voxel, n_tr)
      tfa.fit(X, R=R)
      assert True, "Success running TFA with one subject!"
  
      template_prior, _, _ = tfa.get_template(R)
      tfa.set_K(K)
      tfa.set_seed(200)
      tfa.fit(X, R=R, template_prior=template_prior)
      assert (
          True
      ), "Success running TFA with one subject and template prior!"
      assert (
          tfa.local_posterior_.shape[0] == posterior_size
      ), "Invalid result of TFA! (wrong # element in local_posterior)"
  
      weight_method = "odd"
      tfa = TFA(
          weight_method=weight_method,
          K=K,
          max_iter=max_iter,
          verbose=True,
          max_num_voxel=max_num_voxel,
          max_num_tr=max_num_tr,
      )
      with pytest.raises(ValueError) as excinfo:
          tfa.fit(X, R=R)
      assert "'rr' and 'ols' are accepted as weight_method!" in str(
          excinfo.value
      )
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_tfa() without return types