code: def map_coordinates(
      input, coordinates, order, fill_mode="constant", fill_value=0
  ):
      """Map the input array to new coordinates by interpolation..
  
      Note that interpolation near boundaries differs from the scipy function,
      because we fixed an outstanding bug
      [scipy/issues/2640](https://github.com/scipy/scipy/issues/2640).
  
      Args:
          input: The input array.
          coordinates: The coordinates at which input is evaluated.
          order: The order of the spline interpolation. The order must be `0` or
              `1`. `0` indicates the nearest neighbor and `1` indicates the linear
              interpolation.
          fill_mode: Points outside the boundaries of the input are filled
              according to the given mode. Available methods are `"constant"`,
              `"nearest"`, `"wrap"` and `"mirror"` and `"reflect"`. Defaults to
              `"constant"`.
              - `"constant"`: `(k k k k | a b c d | k k k k)`
                  The input is extended by filling all values beyond
                  the edge with the same constant value k specified by
                  `fill_value`.
              - `"nearest"`: `(a a a a | a b c d | d d d d)`
                  The input is extended by the nearest pixel.
              - `"wrap"`: `(a b c d | a b c d | a b c d)`
                  The input is extended by wrapping around to the opposite edge.
              - `"mirror"`: `(c d c b | a b c d | c b a b)`
                  The input is extended by mirroring about the edge.
              - `"reflect"`: `(d c b a | a b c d | d c b a)`
                  The input is extended by reflecting about the edge of the last
                  pixel.
          fill_value: Value used for points outside the boundaries of the input if
              `fill_mode="constant"`. Defaults to `0`.
  
      Returns:
          Output image or batch of images.
  
      """
      if any_symbolic_tensors((input, coordinates)):
          return MapCoordinates(
              order,
              fill_mode,
              fill_value,
          ).symbolic_call(input, coordinates)
      return backend.image.map_coordinates(
          input,
          coordinates,
          order,
          fill_mode,
          fill_value,
      )
  
imports: from keras import backend
  from keras import ops
  from keras.api_export import keras_export
  from keras.backend import KerasTensor
  from keras.backend import any_symbolic_tensors
  from keras.ops.operation import Operation
  from keras.ops.operation_utils import compute_conv_output_shape
label_desc: Use image node_id 5 for calling a global function with example usage: map_coordinates(input, coordinates, order, fill_mode, fill_value) and returns: backend, MapCoordinates