code: def test_input_req(self, same_inputs):
      test_utils.skip_test(self)
  
      key = random.PRNGKey(1)
      x1 = random.normal(key, (2, 7, 8, 4, 3))
      x2 = None if same_inputs else random.normal(key, (4, 7, 8, 4, 3))
  
      _, _, wrong_conv_fn = stax.serial(
          stax.Conv(
              out_chan=1,
              filter_shape=(1, 2, 3),
              dimension_numbers=("NDHWC", "HDWIO", "NCDWH"),
          ),
          stax.Relu(),
          stax.Conv(
              out_chan=1,
              filter_shape=(1, 2, 3),
              dimension_numbers=("NHDWC", "HWDIO", "NCWHD"),
          ),
      )
      with self.assertRaises(ValueError):
          wrong_conv_fn(x1, x2)
  
      init_fn, apply_fn, correct_conv_fn = stax.serial(
          stax.Conv(
              out_chan=1024,
              filter_shape=(1, 2, 3),
              dimension_numbers=("NHWDC", "DHWIO", "NCWDH"),
          ),
          stax.Relu(),
          stax.Conv(
              out_chan=1024,
              filter_shape=(1, 2, 3),
              dimension_numbers=("NCHDW", "WHDIO", "NCDWH"),
          ),
          stax.Flatten(),
          stax.Dense(1024),
      )
  
      correct_conv_fn_mc = nt.monte_carlo_kernel_fn(
          init_fn=init_fn,
          apply_fn=stax.unmask_fn(apply_fn),
          key=key,
          n_samples=400,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          vmap_axes=0,
      )
      K = correct_conv_fn(x1, x2, get="nngp")
      K_mc = correct_conv_fn_mc(x1, x2, get="nngp")
      self.assertAllClose(K, K_mc, atol=0.01, rtol=0.05)
  
      _, _, wrong_conv_fn = stax.serial(
          stax.Conv(
              out_chan=1,
              filter_shape=(1, 2, 3),
              dimension_numbers=("NDHWC", "HDWIO", "NCDWH"),
          ),
          stax.GlobalAvgPool(channel_axis=2),
      )
      with self.assertRaises(ValueError):
          wrong_conv_fn(x1, x2)
  
      init_fn, apply_fn, correct_conv_fn = stax.serial(
          stax.Conv(
              out_chan=1024,
              filter_shape=(1, 2, 3),
              dimension_numbers=("NHDWC", "DHWIO", "NDWCH"),
          ),
          stax.Relu(),
          stax.AvgPool((2, 1, 3), batch_axis=0, channel_axis=-2),
          stax.Conv(
              out_chan=1024,
              filter_shape=(1, 2, 3),
              dimension_numbers=("NDHCW", "IHWDO", "NDCHW"),
          ),
          stax.Relu(),
          stax.GlobalAvgPool(channel_axis=2),
          stax.Dense(1024),
      )
  
      correct_conv_fn_mc = nt.monte_carlo_kernel_fn(
          init_fn=init_fn,
          apply_fn=stax.unmask_fn(apply_fn),
          key=key,
          n_samples=300,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          vmap_axes=0,
      )
      K = correct_conv_fn(x1, x2, get="nngp")
      K_mc = correct_conv_fn_mc(x1, x2, get="nngp")
      self.assertAllClose(K, K_mc, atol=0.01, rtol=0.05)
  
      _, _, wrong_conv_fn = stax.serial(
          stax.Flatten(),
          stax.Dense(1),
          stax.Erf(),
          stax.Conv(
              out_chan=1,
              filter_shape=(1, 2),
              dimension_numbers=("CN", "IO", "NC"),
          ),
      )
      with self.assertRaises(ValueError):
          wrong_conv_fn(x1, x2)
  
      init_fn, apply_fn, correct_conv_fn = stax.serial(
          stax.Flatten(),
          stax.Conv(out_chan=1024, filter_shape=()),
          stax.Relu(),
          stax.Dense(1),
      )
  
      correct_conv_fn_mc = nt.monte_carlo_kernel_fn(
          init_fn=init_fn,
          apply_fn=stax.unmask_fn(apply_fn),
          key=key,
          n_samples=200,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          vmap_axes=0,
      )
      K = correct_conv_fn(x1, x2, get="ntk")
      K_mc = correct_conv_fn_mc(x1, x2, get="ntk")
      self.assertAllClose(K, K_mc, atol=0.01, rtol=0.05)
  
imports: import itertools
  import random
  from absl.testing import absltest
  from jax import config
  from jax import default_backend
  from jax import jit
  from jax import random
  import jax.numpy
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src.empirical import _DEFAULT_TESTING_NTK_IMPLEMENTATION
  from tests import test_utils
label_desc: Use image node_id 1 for calling the InputReqTest obj's underlying member method code with example usage: obj.test_input_req(same_inputs) without return types