code: def diag_jacobian(
      x: FiniteDifferences, *, stagger=[0], params=None
  ) -> FiniteDifferences:
      r"""Diagonal Jacobian operator for finite differences fields.
  
      Args:
        x: FiniteDifferences field
        stagger: Stagger of the derivative
  
      Returns:
        The diagonal Jacobian of the field
      """
      kernels = params
      array = x.on_grid
  
      # Apply the corresponding kernel to each dimension
      outs = [
          reflection_conv(kernels[i], array[..., i])
          for i in range(x.domain.ndim)
      ]
      new_params = jnp.stack(outs, axis=-1)
  
      return x.replace_params(new_params)
  
imports: from typing import Union
  import jax
  import numpy
  from jax import numpy
  from jaxdf.conv import fd_coefficients_fornberg, reflection_conv
  from jaxdf.core import operator
  from jaxdf.discretization import Continuous, FiniteDifferences, FourierSeries
label_desc: Use image node_id 12 for calling a global function with example usage: diag_jacobian(x) and returns: x