code: def _get_inputs(cls, out_logits, test_shape, train_shape):
      key = random.PRNGKey(0)
      key, split = random.split(key)
      x_train = random.normal(split, train_shape)
      key, split = random.split(key)
      y_train = jnp.array(
          random.bernoulli(split, shape=(train_shape[0], out_logits)),
          jnp.float32,
      )
      key, split = random.split(key)
      x_test = random.normal(split, test_shape)
      return key, x_test, x_train, y_train
  
imports: import math
  from absl.testing import absltest
  from jax import grad
  from jax import jit
  from jax import random
  from jax import vmap
  from jax import config
  from jax.example_libraries import optimizers
  from jax.flatten_util import ravel_pytree
  import jax.numpy
  import jax.tree_util
  import neural_tangents
  from neural_tangents import predict, stax
  from neural_tangents._src.predict import _is_on_cpu
  from tests import test_utils
label_desc: Use image node_id 4 for calling the PredictTest obj's underlying member method code with example usage: obj._get_inputs(cls, out_logits, test_shape, train_shape) and returns: key,  x_test,  x_train,  y_train