code: def test_dot_general_nn(
      self, same_inputs, get, n, is_rhs, do_pool, dot_first
  ):
      if n != 2:
          test_utils.skip_test(self)
  
      width = 2**8
      n_samples = 2**8
      tol = 0.03
      key1, key2, key3 = random.split(random.PRNGKey(1), 3)
  
      mask_constant = 10.0
  
      x_shape = [6, 3, 4, 5][: n - 1] + [1]
      x1 = jnp.cos(random.normal(key1, x_shape))
      mask1 = random.bernoulli(key1, p=0.8, shape=x1.shape)
      x1 = jnp.where(mask1, mask_constant, x1)
  
      if same_inputs:
          x2 = None
      else:
          x2 = jnp.cos(random.normal(key2, x_shape))
          mask2 = random.bernoulli(key2, p=0.4, shape=x2.shape)
          x2 = jnp.where(mask2, mask_constant, x2)
  
      other = random.normal(key3, [3, 4, 6, 2])
  
      c_dims, b_dims = (1,), (0,)
      o_c_dims, o_b_dims = (0,), (2,)
  
      if is_rhs:
          lhs, rhs = None, other
          dn = ((c_dims, o_c_dims), (b_dims, o_b_dims))
      else:
          lhs, rhs = other, None
          dn = ((o_c_dims, c_dims), (o_b_dims, b_dims))
  
      lhs_ndim = None if lhs is None else lhs.ndim
      out_c_axis = utils.axis_after_dot(n - 1, c_dims, b_dims, lhs_ndim)
      out_b_axis = utils.axis_after_dot(0, c_dims, b_dims, lhs_ndim)
  
      top_b_axis = int(out_b_axis > out_c_axis and do_pool)
      init_fn, apply_fn, kernel_fn = stax.serial(
          stax.Identity()
          if dot_first
          else stax.Conv(width, (3,) * (n - 2), padding="SAME"),
          stax.DotGeneral(lhs=lhs, rhs=rhs, dimension_numbers=dn),
          stax.Dense(
              width, batch_axis=out_b_axis, channel_axis=out_c_axis
          ),
          stax.Relu(),
          (
              stax.GlobalAvgPool(
                  channel_axis=out_c_axis, batch_axis=out_b_axis
              )
              if do_pool
              else stax.Flatten(batch_axis=out_b_axis)
          ),
          stax.Dense(
              width if get == "nngp" else 1,
              0.9,
              0.1,
              batch_axis=top_b_axis,
              channel_axis=int(out_c_axis > out_b_axis or not do_pool),
          ),
      )
  
      kernel_fn_mc = nt.monte_carlo_kernel_fn(
          init_fn=init_fn,
          apply_fn=stax.unmask_fn(apply_fn),
          key=key1,
          n_samples=n_samples,
          trace_axes=(int(out_c_axis > out_b_axis) if do_pool else 1,),
          device_count=0,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
      )
  
      empirical = kernel_fn_mc(x1, x2, get, mask_constant=mask_constant)
      exact = kernel_fn(x1, x2, get, mask_constant=mask_constant)
      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 2 for calling the DotGeneralTest obj's underlying member method code with example usage: obj.test_dot_general_nn(same_inputs, get, n, is_rhs, do_pool, dot_first) without return types