code: def get_fd_coefficients(
      x: FiniteDifferences,
      order: int = 1,
      stagger: Union[float, int] = 0,
  ):
      r"""Returnst the stencil coefficients for a 1D Finite Differences derivative
      operator.
  
      Args:
        x: FiniteDifferences field
        order: Order of the derivative
        stagger: Stagger of the derivative
  
      Returns:
        Stencil coefficients
      """
  
      # Check that all the values of stagger are in [0, 0.5, -0.5]
      # assert stagger in [0, -0.5, 0.5], f'Staggering must be in [0, 0.5, -0.5] for finite differences, got {stagger}'
      accuracy = x.accuracy
      points = np.arange(-accuracy // 2, accuracy // 2 + 1)
      if stagger > 0:
          points = (points + stagger)[:-1]
      elif stagger < 0:
          points = (points + stagger)[1:]
  
      # get coefficients
      coeffs = fd_coefficients_fornberg(order, points, x0=0)[0].tolist()
  
      # Append zero if a coefficient has been removed
      if stagger > 0:
          coeffs = coeffs + [0.0]
      elif stagger < 0:
          coeffs = [0.0] + coeffs
  
      return np.asarray(coeffs)
  
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 3 for calling a global function with example usage: get_fd_coefficients(x, order, stagger) and returns: np