code: def parallel(*layers: Layer) -> InternalLayer:
      """Combinator for composing layers in parallel.
  
      The layer resulting from this combinator is often used with the
      :obj:`~neural_tangents.stax.FanOut`, :obj:`~neural_tangents.stax.FanInSum`,
      and :obj:`~neural_tangents.stax.FanInConcat` layers. Based on
      :obj:`jax.example_libraries.stax.parallel`.
  
      Args:
        *layers:
          a sequence of layers, each with a `(init_fn, apply_fn, kernel_fn)` triple.
  
      Returns:
        A new layer, meaning an `(init_fn, apply_fn, kernel_fn)` triples,
        representing the parallel composition of the given sequence of layers. In
        particular, the returned layer takes a sequence of inputs and returns a
        sequence of outputs with the same length as the argument `layers`.
      """
      init_fns, apply_fns, kernel_fns = zip(*layers)
      init_fn_stax, apply_fn_stax = ostax.parallel(
          *zip(init_fns, apply_fns)
      )
  
      def init_fn(rng: jax.Array, input_shape: Shapes):
          return type(input_shape)(init_fn_stax(rng, input_shape))
  
      def apply_fn(params, inputs, **kwargs):
          return type(inputs)(apply_fn_stax(params, inputs, **kwargs))
  
      @requires(**_get_input_req_attr(kernel_fns, fold=op.and_))
      def kernel_fn(ks: NTTrees[Kernel], **kwargs) -> NTTrees[Kernel]:
          return type(ks)(
              f(k, **kwargs) for k, f in zip(ks, kernel_fns)
          )
  
      return init_fn, apply_fn, kernel_fn
  
imports: import operator
  from typing import Any, Callable
  import warnings
  import frozendict
  import jax
  from jax import lax
  from jax import random
  import jax.example_libraries.stax
  from ..utils.kernel import Kernel
  from ..utils.typing import InternalLayer
  from ..utils.typing import Layer
  from ..utils.typing import LayerKernelFn
  from ..utils.typing import NTTree
  from ..utils.typing import NTTrees
  from ..utils.typing import Shapes
  from .requirements import Diagonal
  from .requirements import get_req
  from .requirements import layer
  from .requirements import requires
label_desc: Use image node_id 3 for calling a global function with example usage: parallel() and returns: init_fn,  apply_fn,  kernel_fn, type, type, type