code: def test_aggregate(
      self,
      get,
      readout,
      same_input,
      activation,
      mask_constant,
      shape,
      batch_axis,
      channel_axis,
      agg_axes,
      do_batch,
      implementation,
      to_dense,
  ):
      if len(shape) > 1:
          test_utils.skip_test(self)
  
      if implementation == "SPARSE" and to_dense != "identity":
          raise absltest.SkipTest(
              '`implementation="SPARSE"` ignores '
              "`to_dense` argument."
          )
  
      if get == "cov2" and same_input:
          raise absltest.SkipTest(
              '`get="cov2"` only defined for different inputs.'
          )
  
      if get in ("cov1", "cov2") and do_batch:
          raise absltest.SkipTest(
              "Batching of empirical kernel does not work for "
              "`diagonal_axes != ()`."
          )
  
      batch1, batch2 = 8, 4
      num_channels = 1
      output_dims = 1 if get == "ntk" else 2**6
  
      key = random.PRNGKey(1)
      key, split1, split2 = random.split(key, 3)
  
      x1 = random.normal(split1, (batch1,) + shape + (num_channels,))
      x1 = jnp.moveaxis(x1, (0, -1), (batch_axis, channel_axis))
  
      if same_input:
          x2 = None
      else:
          x2 = random.normal(
              split2, (batch2,) + shape + (num_channels,)
          )
          x2 = jnp.moveaxis(x2, (0, -1), (batch_axis, channel_axis))
  
      if mask_constant is not None:
          key, split1, split2 = random.split(key, 3)
  
          shape1 = list(x1.shape)
          shape1[channel_axis] = 1
          mask1 = random.bernoulli(split1, p=0.3, shape=shape1)
          x1 = jnp.where(mask1, mask_constant, x1)
  
          if x2 is not None:
              shape2 = list(x2.shape)
              shape2[channel_axis] = 1
              mask2 = random.bernoulli(split2, p=0.2, shape=shape2)
              x2 = jnp.where(mask2, mask_constant, x2)
  
      key, split1, split2 = random.split(key, 3)
  
      agg_shape = (
          shape
          if agg_axes is None
          else tuple(x1.shape[a] for a in agg_axes)
      )
      agg_ndim = len(agg_shape)
  
      def sparse_to_dense(pattern):
          if pattern is None:
              return None
  
          pattern = pattern.reshape(
              pattern.shape[:2] + (pattern.shape[2] * 2,)
          )
  
          bsz, n_edges, n_dims = pattern.shape
          batch_range = jnp.broadcast_to(
              jnp.arange(bsz).reshape((bsz, 1, 1)), (bsz, n_edges, 1)
          )
          pattern = jnp.concatenate([batch_range, pattern], 2)
          pattern = pattern.reshape((bsz * n_edges, n_dims + 1))
          out = jnp.zeros(
              (bsz,) + tuple(a for a in agg_shape for _ in (0, 1))
          )
          out = out.at[tuple(pattern.T)].add(1.0)
          out = utils.unzip_axes(out, 1)
          return out
  
      if to_dense == "sparse_to_dense" or implementation == "SPARSE":
  
          def get_sparse_pattern(batch_size, rng):
              n_edges_max = np.prod((1,) + agg_shape) ** 2
              n_edges = prandom.randint(0, n_edges_max)
              pattern = [
                  jnp.zeros((batch_size, n_edges, 0, 2), jnp.int32)
              ]
  
              for d in range(agg_ndim):
                  rng, _ = random.split(rng)
                  n_nodes = agg_shape[d]
                  edges = random.randint(
                      rng, (batch_size, n_edges, 1, 2), 0, n_nodes
                  )
                  pattern += [edges]
  
              pattern = jnp.concatenate(pattern, 2)
  
              mask = random.bernoulli(
                  rng, p=0.2, shape=pattern.shape[:2]
              )
              # Make sure the receivers are masked to large negative number.
              # The number needs to be larger than maximum size of `pattern` along
              # any of the shape axes, to make `jax.ops.at` ignore these entries in
              # `sparse_to_dense` above, otherwise they are treated as regular
              # negative indices.
              pattern = pattern.at[mask].set(-10000)
              return pattern
  
          pattern1 = get_sparse_pattern(batch1, split1)
          pattern2 = (
              pattern1
              if same_input
              else get_sparse_pattern(batch2, split2)
          )
  
      else:
          pattern1 = random.uniform(split1, (batch1,) + agg_shape * 2)
          pattern2 = (
              pattern1
              if same_input
              else random.uniform(split2, (batch2,) + agg_shape * 2)
          )
  
      # Build the infinite network.
      def get_nn(to_dense, implementation):
          return stax.serial(
              stax.Dense(
                  2**6,
                  batch_axis=batch_axis,
                  channel_axis=channel_axis,
              ),
              activation,
              stax.Aggregate(
                  aggregate_axis=agg_axes,
                  batch_axis=batch_axis,
                  channel_axis=channel_axis,
                  to_dense=dict(
                      identity=lambda p: p,
                      sparse_to_dense=sparse_to_dense,
                  )[to_dense],
                  implementation=implementation,
              ),
              readout,
              stax.Dense(
                  output_dims,
                  batch_axis=int(batch_axis > channel_axis),
                  channel_axis=int(batch_axis < channel_axis),
              ),
          )
  
      init_fn, apply_fn, kernel_fn = get_nn(to_dense, implementation)
      apply_fn = jit(apply_fn)
      kernel_fn = jit(kernel_fn, static_argnames="get")
  
      if do_batch:
          kernel_fn = nt.batch(kernel_fn, batch_size=2)
  
      exact = kernel_fn(
          x1,
          x2,
          get,
          mask_constant=mask_constant,
          pattern=(pattern1, pattern2),
      )
  
      rtol = 0.08
  
      if to_dense == "sparse_to_dense" or implementation == "SPARSE":
          init_fn_dense, apply_fn_dense, kernel_fn_dense = get_nn(
              "identity", "DENSE"
          )
          apply_fn_dense = jit(apply_fn_dense)
          kernel_fn_dense = jit(kernel_fn_dense, static_argnames="get")
  
          pattern1_dense = sparse_to_dense(pattern1)
          pattern2_dense = sparse_to_dense(pattern2)
  
          # Test parameters agreement
          key, _ = random.split(key, 2)
          _, params_sparse = init_fn(key, x1.shape)
          _, params_dense = init_fn_dense(key, x1.shape)
          self.assertAllClose(params_dense, params_sparse)
  
          # Test forward-pass agreement
          fx1_dense = apply_fn_dense(
              params_dense, x1, pattern=pattern1_dense
          )
          fx1_sparse = apply_fn(params_sparse, x1, pattern=pattern1)
          test_utils.assert_close_matrices(
              self, fx1_dense, fx1_sparse, rtol
          )
  
          if not same_input:
              fx2_dense = apply_fn_dense(
                  params_dense, x2, pattern=pattern2_dense
              )
              fx2_sparse = apply_fn(params_sparse, x2, pattern=pattern2)
              test_utils.assert_close_matrices(
                  self, fx2_dense, fx2_sparse, rtol
              )
  
          # Test agreement with analytic dense kernel
          exact_dense = kernel_fn_dense(
              x1,
              x2,
              get,
              mask_constant=mask_constant,
              pattern=(pattern1_dense, pattern2_dense),
          )
  
          self.assertAllClose(exact_dense, exact)
  
      # Test agreement with empirical kernel
      kernel_mc_fn = nt.monte_carlo_kernel_fn(
          init_fn=init_fn,
          apply_fn=stax.unmask_fn(apply_fn),
          key=random.PRNGKey(10),
          n_samples=2**6,
          batch_size=2
          if (default_backend() == "tpu" and batch_axis == 0)
          else 0,
          device_count=-1 if batch_axis == 0 else 0,
          implementation=_DEFAULT_TESTING_NTK_IMPLEMENTATION,
          trace_axes=(int(batch_axis < channel_axis),),
      )
  
      if get in ("nngp", "ntk"):
          empirical = kernel_mc_fn(
              x1,
              x2,
              get,
              mask_constant=mask_constant,
              pattern=(pattern1, pattern2),
          )
  
      elif get in ("cov1", "cov2"):
          if get == "cov1":
              empirical = kernel_mc_fn(
                  x1,
                  None,
                  "nngp",
                  mask_constant=mask_constant,
                  pattern=(pattern1, pattern1),
              )
  
          elif get == "cov2":
              empirical = kernel_mc_fn(
                  x2,
                  None,
                  "nngp",
                  mask_constant=mask_constant,
                  pattern=(pattern2, pattern2),
              )
  
          empirical = jnp.moveaxis(jnp.diagonal(empirical), -1, 0)
  
      else:
          raise ValueError(get)
  
      test_utils.assert_close_matrices(
          self, exact, empirical, rtol, 0.2
      )
  
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 1 for calling the AggregateTest obj's underlying member method code with example usage: obj.test_aggregate(get, readout, same_input, activation, mask_constant, shape, batch_axis, channel_axis, agg_axes, do_batch, implementation, to_dense) and returns: out, stax, None, pattern