code: def _preprocess_kernels_for_fan_in(
      ks: Kernels,
  ) -> tuple[list[Kernel], bool]:
      # Check diagonal requirements.
      if not all(
          k.diagonal_batch == ks[0].diagonal_batch
          and k.diagonal_spatial == ks[0].diagonal_spatial
          and k.batch_axis == ks[0].batch_axis
          and k.channel_axis == ks[0].channel_axis
          for k in ks[1:]
      ):
          raise NotImplementedError(
              "`FanIn` layers are only implemented for the "
              "case if all input layers output the same layout "
              "of covariance matrices, i.e. having all "
              "matching `diagonal_batch` and "
              "`diagonal_spatial` and other attributes."
          )
  
      # If kernels have different spatial axes order, transpose some of them.
      n_kernels = len(ks)
      n_reversed = sum(ker.is_reversed for ker in ks)
      ks = list(ks)
  
      if n_reversed > n_kernels / 2:
          is_reversed = True
          for i in range(n_kernels):
              if not ks[i].is_reversed:
                  ks[i] = ks[i].reverse()
  
      else:
          is_reversed = False
          for i in range(n_kernels):
              if ks[i].is_reversed:
                  ks[i] = ks[i].reverse()
  
      # Warnings.
      warnings.warn(
          "`FanIn` layers assume independent inputs which is not verified"
          " in the code. Please make sure to have at least one `Dense` / "
          "`Conv` / `GlobalSelfAttention` etc. layer in each branch."
      )
  
      return ks, is_reversed
  
imports: import functools
  from typing import Callable, Iterable, Optional, Sequence
  import warnings
  from jax import numpy
  import jax.example_libraries.stax
  from ..utils.kernel import Kernel
  from ..utils.typing import InternalLayer
  from ..utils.typing import InternalLayerMasked
  from ..utils.typing import Kernels
  from .requirements import layer
  from .requirements import supports_masking
label_desc: Use image node_id 8 for calling a global function with example usage: _preprocess_kernels_for_fan_in(ks) and returns: ks,  is_reversed