code: def fd_derivative_init(
      x: FiniteDifferences, axis=0, stagger=0, *args, **kwargs
  ):
      r"""Initializes the stencils for FiniteDifferences derivatives. Accepts
      an arbitrary number of positional and keyword arguments after the
      mandatory arguments, which are ignored.
  
      Args:
        x: FiniteDifferences field
        axis: Axis along which to take the derivative
        stagger: Stagger of the derivative
  
      Returns:
        Stencil coefficients
      """
      kernel = get_fd_coefficients(x, order=1, 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)
  
      # Add dx
      kernel = kernel / x.domain.dx[axis]
  
      return kernel
  
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 4 for calling a global function with example usage: fd_derivative_init(x, axis, stagger) and returns: kernel