code: def _fixed_map_coordinates(
      input, coordinates, order, fill_mode="constant", fill_value=0.0
  ):
      # SciPy's implementation of map_coordinates handles boundaries incorrectly,
      # unless mode='reflect'. For order=1, this only affects interpolation
      # outside the bounds of the original array.
      # https://github.com/scipy/scipy/issues/2640
      padding = [
          (
              max(-np.floor(c.min()).astype(int) + 1, 0),
              max(np.ceil(c.max()).astype(int) + 1 - size, 0),
          )
          for c, size in zip(coordinates, input.shape)
      ]
      shifted_coords = [c + p[0] for p, c in zip(padding, coordinates)]
      pad_mode = {
          "nearest": "edge",
          "mirror": "reflect",
          "reflect": "symmetric",
      }.get(fill_mode, fill_mode)
      if fill_mode == "constant":
          padded = np.pad(
              input, padding, mode=pad_mode, constant_values=fill_value
          )
      else:
          padded = np.pad(input, padding, mode=pad_mode)
      result = scipy.ndimage.map_coordinates(
          padded,
          shifted_coords,
          order=order,
          mode=fill_mode,
          cval=fill_value,
      )
      return result
  
imports: import math
  import numpy
  import pytest
  import scipy.ndimage
  import tensorflow
  from absl.testing import parameterized
  from keras import backend
  from keras import testing
  from keras.backend.common.keras_tensor import KerasTensor
  from keras.ops import image
label_desc: Use image node_id 2 for calling a global function with example usage: _fixed_map_coordinates(input, coordinates, order, fill_mode, fill_value) and returns: result