code: def test_dot_general_mask(self):
      x1, x2 = jnp.ones((4, 2, 3, 1)), jnp.ones((4, 2, 3, 1))
  
      mask_constant = 10.0
  
      def get_k(x1, x2, m1, m2):
          x1 = jnp.where(m1, mask_constant, x1)
          x2 = jnp.where(m2, mask_constant, x2)
          k_fn = stax.DotGeneral(
              rhs=jnp.ones(x1.shape[:-1]),
              dimension_numbers=(((1,), (1,)), ((2,), (2,))),
          )[2]
          k = k_fn(x1, x2, "nngp", mask_constant=mask_constant)
          return k
  
      m1, m2 = jnp.zeros_like(x1, jnp.bool_), jnp.zeros_like(
          x2, jnp.bool_
      )
      k = get_k(x1, x2, m1, m2)
      self.assertAllClose(jnp.ones_like(k) * 4, k)
  
      m1, m2 = jnp.ones_like(x1, jnp.bool_), jnp.zeros_like(
          x2, jnp.bool_
      )
      k = get_k(x1, x2, m1, m2)
      self.assertAllClose(jnp.zeros_like(k), k)
  
      m1, m2 = jnp.ones_like(x1, jnp.bool_), jnp.ones_like(
          x2, jnp.bool_
      )
      k = get_k(x1, x2, m1, m2)
      self.assertAllClose(jnp.zeros_like(k), k)
  
      m1 = jnp.concatenate(
          [
              jnp.ones_like(x1[:2], jnp.bool_),
              jnp.zeros_like(x1[2:], jnp.bool_),
          ]
      )
      m2 = jnp.zeros_like(x2, jnp.bool_)
      k = get_k(x1, x2, m1, m2)
      self.assertAllClose(jnp.zeros_like(k[:2]), k[:2])
      self.assertAllClose(jnp.full_like(k[2:], 4.0), k[2:])
  
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 3 for calling the DotGeneralTest obj's underlying member method code with example usage: obj.test_dot_general_mask() and returns: k