code: def fd_diag_jacobian_init(
      x: FiniteDifferences, stagger, *args, **kwargs
  ):
      r"""Initializes the parameters for the diagonal Jacobian of a FiniteDifferences field. Accepts
      an arbitrary number of positional and keyword arguments after the
      mandatory arguments, which are ignored.
  
      Args:
        x: FiniteDifferences field
        stagger: Stagger of the derivative
  
      Returns:
        Stencil coefficients
  
      """
      if len(stagger) != x.domain.ndim:
          stagger = [stagger[0] for _ in range(x.domain.ndim)]
  
      kernels = []
      for i in range(x.domain.ndim):
          kernels.append(
              fd_derivative_init(x, axis=i, stagger=stagger[i])
          )
  
      return kernels
  
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 5 for calling a global function with example usage: fd_diag_jacobian_init(x, stagger) and returns: kernels