code: def test_attention(
      self,
      same_inputs,
      get,
      n,
      linear_scaling,
      mask_constant,
      p,
      mask_axis,
      pos_emb_type,
      n_chan_pos_emb,
      pos_emb_decay_fn,
      val_pos_emb,
      W_pos_emb_std,
  ):
      test_utils.skip_test(self)
  
      width = 1024
      n_samples = 1024
      tol = 0.05
      key = random.PRNGKey(1)
      n_chan_in = 2
      spatial_shape = (2, 3, 4, 3, 2, 1)[:n]
      mask_axis = [i % (n + 2) for i in mask_axis]
  
      def get_x0(batch_size):
          x0 = random.normal(
              key, (batch_size,) + spatial_shape + (n_chan_in,)
          )
          x0 = test_utils.mask(x0, mask_constant, mask_axis, key, p)
          return x0
  
      X0_1 = get_x0(2)
      X0_2 = None if same_inputs else get_x0(4)
  
      pos_emb_fns = {
          None: None,
          "one_hot": lambda x: x == 0,
          "linear": lambda x: 1 / (1 + 4 * x),
      }
  
      def get_attn():
          return stax.GlobalSelfAttention(
              linear_scaling=linear_scaling,
              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)),
              n_chan_pos_emb=n_chan_pos_emb,
              attention_mechanism="SOFTMAX"
              if linear_scaling
              else "IDENTITY",
              pos_emb_type=pos_emb_type,
              W_pos_emb_std=W_pos_emb_std,
              pos_emb_decay_fn=pos_emb_fns[pos_emb_decay_fn],
              val_pos_emb=val_pos_emb,
              W_key_std=0.9,
              W_out_std=0.8,
              W_query_std=0.7,
              W_value_std=1.2,
              b_std=0.5,
          )
  
      nn = stax.serial(
          stax.Conv(width, (1,) * n, padding="SAME"),
          get_attn(),
          stax.Relu(),
          stax.GlobalAvgPool(),
      )
  
      if get == "nngp":
          init_fn, apply_fn, kernel_fn = nn
      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=-1,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          vmap_axes=0,
      )
  
      kernel_fn = jit(kernel_fn, static_argnames="get")
      exact = kernel_fn(X0_1, X0_2, get, mask_constant=mask_constant)
  
      empirical = kernel_fn_mc(
          X0_1, X0_2, get=get, mask_constant=mask_constant
      )
      test_utils.assert_close_matrices(self, empirical, exact, tol, 2.0)
  
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 AttentionTest obj's underlying member method code with example usage: obj.test_attention(same_inputs, get, n, linear_scaling, mask_constant, p, mask_axis, pos_emb_type, n_chan_pos_emb, pos_emb_decay_fn, val_pos_emb, W_pos_emb_std) and returns: x0, stax