code: def ediff1d(ary, to_end=None, to_begin=None):
      """
      The differences between consecutive elements of an array.
  
      Parameters
      ----------
      ary : array_like
          If necessary, will be flattened before the differences are taken.
      to_end : array_like, optional
          Number(s) to append at the end of the returned differences.
      to_begin : array_like, optional
          Number(s) to prepend at the beginning of the returned differences.
  
      Returns
      -------
      ediff1d : ndarray
          The differences. Loosely, this is ``ary.flat[1:] - ary.flat[:-1]``.
  
      See Also
      --------
      diff, gradient
  
      Notes
      -----
      When applied to masked arrays, this function drops the mask information
      if the `to_begin` and/or `to_end` parameters are used.
  
      Examples
      --------
      >>> x = np.array([1, 2, 4, 7, 0])
      >>> np.ediff1d(x)
      array([ 1,  2,  3, -7])
  
      >>> np.ediff1d(x, to_begin=-99, to_end=np.array([88, 99]))
      array([-99,   1,   2, ...,  -7,  88,  99])
  
      The returned array is always 1D.
  
      >>> y = [[1, 2, 4], [1, 6, 24]]
      >>> np.ediff1d(y)
      array([ 1,  2, -3,  5, 18])
  
      """
      # force a 1d array
      ary = np.asanyarray(ary).ravel()
  
      # enforce that the dtype of `ary` is used for the output
      dtype_req = ary.dtype
  
      # fast track default case
      if to_begin is None and to_end is None:
          return ary[1:] - ary[:-1]
  
      if to_begin is None:
          l_begin = 0
      else:
          to_begin = np.asanyarray(to_begin)
          if not np.can_cast(to_begin, dtype_req, casting="same_kind"):
              raise TypeError(
                  "dtype of `to_begin` must be compatible "
                  "with input `ary` under the `same_kind` rule."
              )
  
          to_begin = to_begin.ravel()
          l_begin = len(to_begin)
  
      if to_end is None:
          l_end = 0
      else:
          to_end = np.asanyarray(to_end)
          if not np.can_cast(to_end, dtype_req, casting="same_kind"):
              raise TypeError(
                  "dtype of `to_end` must be compatible "
                  "with input `ary` under the `same_kind` rule."
              )
  
          to_end = to_end.ravel()
          l_end = len(to_end)
  
      # do the calculation in place and copy to_begin and to_end
      l_diff = max(len(ary) - 1, 0)
      result = np.empty(l_diff + l_begin + l_end, dtype=ary.dtype)
      result = ary.__array_wrap__(result)
      if l_begin > 0:
          result[:l_begin] = to_begin
      if l_end > 0:
          result[l_begin + l_diff :] = to_end
      np.subtract(ary[1:], ary[:-1], result[l_begin : l_begin + l_diff])
      return result
  
imports: import functools
  import warnings
  from typing import NamedTuple
  import numpy
  from numpy._core import overrides
label_desc: Use image node_id 2 for calling a global function with example usage: ediff1d(ary, to_end, to_begin) and returns: result, unknown