code: def resize(
      image,
      size,
      interpolation="bilinear",
      antialias=False,
      data_format="channels_last",
  ):
      """Resize images to size using the specified interpolation method.
  
      Args:
          image: Input image or batch of images. Must be 3D or 4D.
          size: Size of output image in `(height, width)` format.
          interpolation: Interpolation method. Available methods are `"nearest"`,
              `"bilinear"`, and `"bicubic"`. Defaults to `"bilinear"`.
          antialias: Whether to use an antialiasing filter when downsampling an
              image. Defaults to `False`.
          data_format: string, either `"channels_last"` or `"channels_first"`.
              The ordering of the dimensions in the inputs. `"channels_last"`
              corresponds to inputs with shape `(batch, height, width, channels)`
              while `"channels_first"` corresponds to inputs with shape
              `(batch, channels, height, weight)`. It defaults to the
              `image_data_format` value found in your Keras config file at
              `~/.keras/keras.json`. If you never set it, then it will be
              `"channels_last"`.
  
      Returns:
          Resized image or batch of images.
  
      Examples:
  
      >>> x = np.random.random((2, 4, 4, 3)) # batch of 2 RGB images
      >>> y = keras.ops.image.resize(x, (2, 2))
      >>> y.shape
      (2, 2, 2, 3)
  
      >>> x = np.random.random((4, 4, 3)) # single RGB image
      >>> y = keras.ops.image.resize(x, (2, 2))
      >>> y.shape
      (2, 2, 3)
  
      >>> x = np.random.random((2, 3, 4, 4)) # batch of 2 RGB images
      >>> y = keras.ops.image.resize(x, (2, 2),
      ...     data_format="channels_first")
      >>> y.shape
      (2, 3, 2, 2)
      """
  
      if any_symbolic_tensors((image,)):
          return Resize(
              size,
              interpolation=interpolation,
              antialias=antialias,
              data_format=data_format,
          ).symbolic_call(image)
      return backend.image.resize(
          image,
          size,
          interpolation=interpolation,
          antialias=antialias,
          data_format=data_format,
      )
  
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 1 for calling a global function with example usage: resize(image, size, interpolation, antialias, data_format) and returns: backend, Resize