code: def fd_shift_kernels(
      x: FiniteDifferences, dx: List[float], *args, **kwargs
  ):
      r"""Computes the shift kernels for FiniteDifferences fields.
  
      Args:
        x: The field to shift
        dx: The shift to apply
  
      Returns:
        The kernel to apply to the field coefficients in order to
        shift the field.
      """
  
      def single_kernel(axis, stagger):
          kernel = get_fd_coefficients(x, order=0, stagger=stagger)
          if x.domain.ndim > 1:
              for _ in range(x.domain.ndim - 1):
                  kernel = np.expand_dims(kernel, axis=0)
              # Move kernel to the correct axis
              kernel = np.moveaxis(kernel, -1, axis)
          # kernel = kernel / x.domain.dx[axis]
          return kernel
  
      stagger = dx[0] / x.domain.dx[0]
      params = []
      for i in range(x.domain.ndim):
          params.append(single_kernel(axis=i, stagger=stagger))
  
      return params
  
imports: from typing import List
  import numpy
  from jax import numpy
  from jaxdf.conv import reflection_conv
  from jaxdf.core import operator
  from jaxdf.discretization import Continuous, FiniteDifferences, FourierSeries, OnGrid
  from jaxdf.operators.differential import get_fd_coefficients
label_desc: Use image node_id 11 for calling a global function with example usage: fd_shift_kernels(x, dx) and returns: params, kernel