code: def RectifiedMonomial(degree: int) -> InternalLayer:
      """Rectified monomials, i.e. `(x >= 0) * x^degree`.
  
      Args:
        degree: a non-negative integer power.
  
      Returns:
        `(init_fn, apply_fn, kernel_fn)`.
      """
      if degree < 0:
          raise NotImplementedError(
              "`degree` must be a non-negative integer."
          )
  
      def fn(x):
          return (x >= 0) * x**degree
  
      def kernel_fn(k: Kernel) -> Kernel:
          cov1, nngp, cov2, ntk = k.cov1, k.nngp, k.cov2, k.ntk
  
          prod11, prod12, prod22 = get_diagonal_outer_prods(
              cov1, cov2, k.diagonal_batch, k.diagonal_spatial, op.mul
          )
  
          def j(
              nngp: jnp.ndarray, sqrt_prod: jnp.ndarray
          ) -> jnp.ndarray:
              theta = jnp.arccos(nngp / sqrt_prod)
  
              def f0(theta: jnp.ndarray) -> jnp.ndarray:
                  return (jnp.pi - theta) / jnp.sin(theta)
  
              def diff(
                  f: Callable[[jnp.ndarray], jnp.ndarray]
              ) -> Callable[[jnp.ndarray], jnp.ndarray]:
                  def df(theta: jnp.ndarray) -> jnp.ndarray:
                      return jnp.vectorize(grad(f))(theta) / jnp.sin(
                          theta
                      )
  
                  return df
  
              f = f0
              for _ in range(degree):
                  f = diff(f)
  
              return (
                  (-1) ** degree
                  * (jnp.sin(theta)) ** (2 * degree + 1)
                  * f(theta)
              )
  
          def nngp_ntk_fn(
              nngp: jnp.ndarray,
              prod: jnp.ndarray,
              ntk: Optional[jnp.ndarray] = None,
          ) -> tuple[jnp.ndarray, Optional[jnp.ndarray]]:
              sqrt_prod = _sqrt(prod)
              coeff = sqrt_prod**degree / (2 * jnp.pi)
  
              if ntk is not None:
                  if degree == 0:
                      ntk = jnp.zeros_like(ntk)
                  else:
                      j_dot = jnp.vectorize(grad(j))(nngp, sqrt_prod)
                      ntk *= coeff * j_dot
  
              nngp = coeff * j(nngp, sqrt_prod)
              return nngp, ntk
  
          def nngp_fn_diag(nngp: jnp.ndarray) -> jnp.ndarray:
              return (
                  _double_factorial(2 * degree - 1) * nngp**degree / 2
              )
  
          nngp, ntk = nngp_ntk_fn(nngp, prod12, ntk)
  
          if k.diagonal_batch and k.diagonal_spatial:
              cov1 = nngp_fn_diag(cov1)
              if cov2 is not None:
                  cov2 = nngp_fn_diag(cov2)
          else:
              cov1, _ = nngp_ntk_fn(cov1, prod11)
              if cov2 is not None:
                  cov2, _ = nngp_ntk_fn(cov2, prod22)
  
          k = k.replace(cov1=cov1, nngp=nngp, cov2=cov2, ntk=ntk)
          return k
  
      return _elementwise(fn, f"{degree}-rectified-monomial", kernel_fn)
  
imports: import functools
  import operator
  from typing import Callable, Optional, Sequence
  import warnings
  import jax
  from jax import custom_jvp
  from jax import grad
  from jax import numpy
  from jax import vmap
  from jax.scipy.special import erf
  import numpy
  import scipy
  from ..utils import utils
  from ..utils.kernel import Kernel
  from ..utils.typing import InternalLayer
  from ..utils.typing import LayerKernelFn
  from .requirements import Diagonal
  from .requirements import get_diagonal
  from .requirements import get_diagonal_outer_prods
  from .requirements import layer
  from .requirements import requires
  from .requirements import supports_masking
label_desc: Use image node_id 18 for calling a global function with example usage: RectifiedMonomial(degree) and returns: _elementwise, unknown, k, unknown, nngp,  ntk, unknown, unknown, df, unknown