code: def test_conv_nd(
      self,
      same_inputs,
      n,
      get,
      proj,
      use_attn,
      channels_first,
      use_dropout,
      use_layernorm,
  ):
      platform = default_backend()
      if platform == "cpu":
          test_utils.skip_test(self)
      elif platform == "gpu" and n not in (0, 1, 2, 3):
          raise absltest.SkipTest(">=4D CNN does not work on GPU.")
      elif platform == "tpu" and use_dropout and same_inputs:
          raise absltest.SkipTest(
              "Batched empirical kernel with dropout not " "supported."
          )
  
      width = 1024
      n_samples = 512
      tol = 0.03 if platform == "tpu" else 0.015
      key = random.PRNGKey(1)
  
      n_max = 5
      spatial_shape = (2, 3, 5, 4, 3)[:n] + (1,) * (n - n_max)
      filter_shape = (1, 2, 3, 1, 1)[:n] + (1,) * (n - n_max)
      strides = (1, 1, 2, 1, 2)[:n] + (1,) * (n - n_max)
      spatial_spec = "".join(
          c
          for c in string.ascii_uppercase
          if c not in ("N", "C", "I", "O")
      )[:n]
      filter_spec = spatial_spec + "IO"
  
      if channels_first:
          channel_axis = 1
          dimension_numbers = (
              "NC" + spatial_spec,
              filter_spec,
              "NC" + spatial_spec,
          )
          X0_1 = random.normal(key, (2, 3) + spatial_shape)
          X0_2 = (
              None
              if same_inputs
              else random.normal(key, (4, 3) + spatial_shape)
          )
      else:
          channel_axis = -1
          dimension_numbers = (
              "N" + spatial_spec + "C",
              filter_spec,
              "N" + spatial_spec + "C",
          )
          X0_1 = random.normal(key, (2,) + spatial_shape + (3,))
          X0_2 = (
              None
              if same_inputs
              else random.normal(key, (4,) + spatial_shape + (3,))
          )
  
      layernorm_axes = (dimension_numbers[2].index("C"),)
      if "H" in dimension_numbers[2]:
          layernorm_axes += (dimension_numbers[2].index("H"),)
  
      if proj == "pool":
          proj = stax.GlobalAvgPool(channel_axis=channel_axis)
      elif proj == "flatten":
          proj = stax.Flatten()
      else:
          raise ValueError(proj)
  
      if use_attn:
          n_heads = int(jnp.sqrt(width))
          n_chan_val = int(jnp.round(float(width) / n_heads))
          proj = stax.serial(
              stax.GlobalSelfAttention(
                  n_chan_out=width,
                  n_chan_key=width,
                  n_chan_val=n_chan_val,
                  n_heads=n_heads,
                  linear_scaling=True,
                  W_key_std=2.0,
                  W_value_std=1.0,
                  W_query_std=1.0,
                  W_out_std=1.0,
                  b_std=0.1,
                  channel_axis=channel_axis,
              ),
              proj,
          )
  
      nn = stax.serial(
          stax.Conv(
              width,
              filter_shape,
              None,
              "SAME",
              dimension_numbers=dimension_numbers,
          ),
          (
              stax.LayerNorm(layernorm_axes, channel_axis=channel_axis)
              if use_layernorm
              else stax.Identity()
          ),
          stax.Relu(),
          (stax.Dropout(0.8) if use_dropout else stax.Identity()),
          stax.Conv(
              width,
              filter_shape,
              strides,
              "CIRCULAR",
              dimension_numbers=dimension_numbers,
          ),
          stax.Abs(),
          proj,
      )
  
      if get == "nngp":
          init_fn, apply_fn, kernel_fn = stax.serial(
              nn, stax.Dense(width, 2.0, 0.5)
          )
      elif get == "ntk":
          init_fn, apply_fn, kernel_fn = stax.serial(
              nn, stax.Dense(1, 2.0, 0.5)
          )
      else:
          raise ValueError(get)
  
      kernel_fn_mc = nt.monte_carlo_kernel_fn(
          init_fn,
          apply_fn,
          key,
          n_samples,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          vmap_axes=0,
      )
  
      exact = kernel_fn(X0_1, X0_2, get=get)
      empirical = kernel_fn_mc(X0_1, X0_2, get=get)
      test_utils.assert_close_matrices(self, empirical, exact, tol)
  
imports: import itertools
  import random
  import string
  import time
  from absl.testing import absltest
  from jax import default_backend
  from jax import jit
  from jax import lax
  from jax import random
  from jax import vjp
  from jax import config
  import jax.numpy
  import more_itertools
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src.empirical import _DEFAULT_TESTING_NTK_IMPLEMENTATION
  from neural_tangents._src.utils import utils
  from tests import test_utils
  import numpy
label_desc: Use image node_id 1 for calling the ConvNDTest obj's underlying member method code with example usage: obj.test_conv_nd(same_inputs, n, get, proj, use_attn, channels_first, use_dropout, use_layernorm) without return types