code: def test_monte_carlo_generator(
      self, batch_size, device_count, store_on_device, get
  ):
      test_utils.stub_out_pmap(batching, device_count)
  
      (
          x1,
          x2,
          init_fn,
          apply_fn,
          stax_kernel_fn,
          key,
      ) = _get_inputs_and_model(8, 1)
      x3, x4, _, _, _, _ = _get_inputs_and_model(8, 1)
  
      log_n_max = 4
      n_samples = [2**k for k in range(log_n_max)]
      sample_generator = monte_carlo.monte_carlo_kernel_fn(
          init_fn,
          apply_fn,
          key,
          n_samples,
          batch_size,
          device_count,
          store_on_device,
          vmap_axes=0,
      )
  
      if get is None:
          samples_12 = sample_generator(x1, x2)
          samples_34 = sample_generator(x3, x4)
  
          count = 0
          for n, s_12, s_34 in zip(n_samples, samples_12, samples_34):
              sample_fn = monte_carlo.monte_carlo_kernel_fn(
                  init_fn,
                  apply_fn,
                  key,
                  n,
                  batch_size,
                  device_count,
                  store_on_device,
                  vmap_axes=0,
              )
              sample_12 = sample_fn(x1, x2)
              sample_34 = sample_fn(x3, x4)
              self.assertAllClose(s_12, sample_12)
              self.assertAllClose(s_12, s_34)
              self.assertAllClose(s_12, sample_34)
              count += 1
  
          self.assertEqual(log_n_max, count)
  
          ker_analytic_12 = stax_kernel_fn(x1, x2, ("nngp", "ntk"))
          ker_analytic_34 = stax_kernel_fn(x3, x4, ("nngp", "ntk"))
  
      else:
          samples_12 = sample_generator(x1, x2, get)
          samples_34 = sample_generator(x3, x4, get)
  
          count = 0
          for n, s_12, s_34 in zip(n_samples, samples_12, samples_34):
              sample_fn = monte_carlo.monte_carlo_kernel_fn(
                  init_fn,
                  apply_fn,
                  key,
                  n,
                  batch_size,
                  device_count,
                  store_on_device,
                  vmap_axes=0,
              )
              sample_12 = sample_fn(x1, x2, get)
              sample_34 = sample_fn(x3, x4, get)
              self.assertAllClose(s_12, sample_12)
              self.assertAllClose(s_12, s_34)
              self.assertAllClose(s_12, sample_34)
              count += 1
  
          self.assertEqual(log_n_max, count)
  
          ker_analytic_12 = stax_kernel_fn(x1, x2, get)
          ker_analytic_34 = stax_kernel_fn(x3, x4, get)
  
      self.assertAllClose(ker_analytic_12, s_12, atol=2.0, rtol=2.0)
      self.assertAllClose(ker_analytic_12, ker_analytic_34)
  
file_path: neural-tangents/tests/monte_carlo_test.py
imports: from absl.testing import absltest
  import jax
  from jax import config
  from jax import random
  import jax.numpy
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src import batching
  from neural_tangents._src import monte_carlo
  from tests import test_utils
label_desc: Use image node_id 5 for calling the MonteCarloTest obj's underlying member method code with example usage: obj.test_monte_carlo_generator(batch_size, device_count, store_on_device, get) without return types