code: def _get_inputs_and_model(width=1, n_classes=2, use_conv=True):
      key = random.PRNGKey(1)
      key, split = random.split(key)
      x1 = random.normal(key, (8, 4, 3, 2))
      x2 = random.normal(split, (4, 4, 3, 2))
  
      if not use_conv:
          x1 = jnp.reshape(x1, (x1.shape[0], -1))
          x2 = jnp.reshape(x2, (x2.shape[0], -1))
  
      init_fn, apply_fn, kernel_fn = stax.serial(
          stax.Conv(width, (3, 3)) if use_conv else stax.Dense(width),
          stax.Relu(),
          stax.Flatten(),
          stax.Dense(n_classes, 2.0, 0.5),
      )
      return x1, x2, init_fn, apply_fn, kernel_fn, key
  
file_path: neural-tangents/tests/monte_carlo_test.py
imports: from absl.testing import absltest
  import jax
  from jax import config
  from jax import random
  import jax.numpy
  import neural_tangents
  from neural_tangents import stax
  from neural_tangents._src import batching
  from neural_tangents._src import monte_carlo
  from tests import test_utils
label_desc: Use image node_id 1 for calling a global function with example usage: _get_inputs_and_model(width, n_classes, use_conv) and returns: x1,  x2,  init_fn,  apply_fn,  kernel_fn,  key