code: def test_mask_conv(
      self,
      same_inputs,
      get,
      mask_axis,
      mask_constant,
      concat,
      proj,
      p,
      n,
      transpose,
  ):
      if isinstance(concat, int) and concat > n:
          raise absltest.SkipTest("Concatenation axis out of bounds.")
  
      test_utils.skip_test(self)
      if default_backend() == "gpu" and n > 3:
          raise absltest.SkipTest(">=4D-CNN is not supported on GPUs.")
  
      width = 128
      n_samples = 256
      tol = 0.04
      key = random.PRNGKey(1)
  
      spatial_shape = ((1, 2, 3, 2, 1) if transpose else (15, 8, 9))[:n]
      filter_shape = ((2, 3, 1, 2, 1) if transpose else (7, 2, 3))[:n]
      strides = (2, 1, 3, 2, 3)[:n]
      spatial_spec = "HWDZX"[:n]
      dimension_numbers = (
          "N" + spatial_spec + "C",
          "OI" + spatial_spec,
          "N" + spatial_spec + "C",
      )
  
      x1 = jnp.cos(random.normal(key, (2,) + spatial_shape + (2,)))
      x1 = test_utils.mask(x1, mask_constant, mask_axis, key, p)
  
      if same_inputs:
          x2 = None
      else:
          x2 = jnp.cos(random.normal(key, (4,) + spatial_shape + (2,)))
          x2 = test_utils.mask(x2, mask_constant, mask_axis, key, p)
  
      def get_attn():
          return (
              stax.GlobalSelfAttention(
                  n_chan_out=width,
                  n_chan_key=width,
                  n_chan_val=int(
                      jnp.round(float(width) / int(jnp.sqrt(width)))
                  ),
                  n_heads=int(jnp.sqrt(width)),
              )
              if proj == "avg"
              else stax.Identity()
          )
  
      conv = stax.ConvTranspose if transpose else stax.Conv
  
      nn = stax.serial(
          stax.FanOut(3),
          stax.parallel(
              stax.serial(
                  conv(
                      dimension_numbers=dimension_numbers,
                      out_chan=width,
                      strides=strides,
                      filter_shape=filter_shape,
                      padding="CIRCULAR",
                      W_std=1.5,
                      b_std=0.2,
                  ),
                  stax.LayerNorm(axis=(1, -1)),
                  stax.Abs(),
                  stax.DotGeneral(rhs=0.9),
                  conv(
                      dimension_numbers=dimension_numbers,
                      out_chan=width,
                      strides=strides,
                      filter_shape=filter_shape,
                      padding="VALID",
                      W_std=1.2,
                      b_std=0.1,
                  ),
              ),
              stax.serial(
                  conv(
                      dimension_numbers=dimension_numbers,
                      out_chan=width,
                      strides=strides,
                      filter_shape=filter_shape,
                      padding="SAME",
                      W_std=0.1,
                      b_std=0.3,
                  ),
                  stax.Relu(),
                  stax.Dropout(0.7),
                  conv(
                      dimension_numbers=dimension_numbers,
                      out_chan=width,
                      strides=strides,
                      filter_shape=filter_shape,
                      padding="VALID",
                      W_std=0.9,
                      b_std=1.0,
                  ),
              ),
              stax.serial(
                  get_attn(),
                  conv(
                      dimension_numbers=dimension_numbers,
                      out_chan=width,
                      strides=strides,
                      filter_shape=filter_shape,
                      padding="CIRCULAR",
                      W_std=1.0,
                      b_std=0.1,
                  ),
                  stax.Erf(),
                  stax.Dropout(0.2),
                  stax.DotGeneral(rhs=0.7),
                  conv(
                      dimension_numbers=dimension_numbers,
                      out_chan=width,
                      strides=strides,
                      filter_shape=filter_shape,
                      padding="VALID",
                      W_std=1.0,
                      b_std=0.1,
                  ),
              ),
          ),
          (
              stax.FanInSum()
              if concat is None
              else stax.FanInConcat(concat)
          ),
          get_attn(),
          {
              "avg": stax.GlobalAvgPool(),
              "sum": stax.GlobalSumPool(),
              "flatten": stax.Flatten(),
          }[proj],
      )
  
      if get == "nngp":
          init_fn, apply_fn, kernel_fn = stax.serial(
              nn, stax.Dense(width, 1.0, 0.0)
          )
      elif get == "ntk":
          init_fn, apply_fn, kernel_fn = stax.serial(
              nn, stax.Dense(1, 1.0, 0.0)
          )
      else:
          raise ValueError(get)
  
      kernel_fn_mc = nt.monte_carlo_kernel_fn(
          init_fn=init_fn,
          apply_fn=stax.unmask_fn(apply_fn),
          key=key,
          n_samples=n_samples,
          device_count=0 if concat in (0, -n) else -1,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          vmap_axes=None if concat in (0, -n) else 0,
      )
  
      kernel_fn = jit(kernel_fn, static_argnames="get")
      exact = kernel_fn(x1, x2, get, mask_constant=mask_constant)
      empirical = kernel_fn_mc(
          x1, x2, get=get, mask_constant=mask_constant
      )
      test_utils.assert_close_matrices(self, empirical, exact, tol, 0.2)
  
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 2 for calling the MaskingTest obj's underlying member method code with example usage: obj.test_mask_conv(same_inputs, get, mask_axis, mask_constant, concat, proj, p, n, transpose) and returns: unknown