code: def ABRelu(
      a: float, b: float, do_stabilize: bool = False
  ) -> InternalLayer:
      """ABReLU nonlinearity, i.e. `a * min(x, 0) + b * max(x, 0)`.
  
      Args:
        a: slope for `x < 0`.
        b: slope for `x > 0`.
        do_stabilize: set to `True` for very deep networks.
  
      Returns:
        `(init_fn, apply_fn, kernel_fn)`.
      """
  
      def fn(x):
          return a * jnp.minimum(x, 0) + b * jnp.maximum(x, 0)
  
      def kernel_fn(k: Kernel) -> Kernel:
          """Compute new kernels after an `ABRelu` layer.
  
          See "`Invariance of Weight Distributions in Rectified MLPs
          <https://arxiv.org/abs/1711.09090>`_" for the leaky ReLU derivation.
          """
          cov1, nngp, cov2, ntk = k.cov1, k.nngp, k.cov2, k.ntk
  
          if do_stabilize:
              factor = jnp.maximum(jnp.max(jnp.abs(nngp)), 1e-12)
              nngp /= factor
              cov1 /= factor
              if cov2 is not None:
                  cov2 /= factor
  
          prod11, prod12, prod22 = get_diagonal_outer_prods(
              cov1, cov2, k.diagonal_batch, k.diagonal_spatial, op.mul
          )
  
          def nngp_ntk_fn(nngp, prod, ntk=None):
              square_root = _sqrt(prod - nngp**2)
              angles = _arctan2(square_root, nngp, fill_zero=jnp.pi / 2)
  
              factor = (a - b) ** 2 / (2 * jnp.pi)
              dot_sigma = (a**2 + b**2) / 2 - factor * angles
              nngp = factor * square_root + dot_sigma * nngp
  
              if ntk is not None:
                  ntk *= dot_sigma
  
              return nngp, ntk
  
          def nngp_fn_diag(nngp):
              return (a**2 + b**2) / 2 * nngp
  
          nngp, ntk = nngp_ntk_fn(nngp, prod12, ntk=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)
  
          if do_stabilize:
              nngp *= factor
              cov1 *= factor
              if cov2 is not None:
                  cov2 *= factor
  
          return k.replace(cov1=cov1, nngp=nngp, cov2=cov2, ntk=ntk)
  
      return _elementwise(fn, f"ABReLU({a}, {b})", 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 8 for calling a global function with example usage: ABRelu(a, b, do_stabilize) and returns: _elementwise, unknown, k, nngp,  ntk, unknown