code: def resize(
      image,
      size,
      interpolation="bilinear",
      antialias=False,
      data_format="channels_last",
  ):
      if interpolation not in RESIZE_INTERPOLATIONS:
          raise ValueError(
              "Invalid value for argument `interpolation`. Expected of one "
              f"{RESIZE_INTERPOLATIONS}. Received: interpolation={interpolation}"
          )
      if not len(size) == 2:
          raise ValueError(
              "Argument `size` must be a tuple of two elements "
              f"(height, width). Received: size={size}"
          )
      size = tuple(size)
      if len(image.shape) == 4:
          if data_format == "channels_last":
              size = (image.shape[0],) + size + (image.shape[-1],)
          else:
              size = (image.shape[0], image.shape[1]) + size
      elif len(image.shape) == 3:
          if data_format == "channels_last":
              size = size + (image.shape[-1],)
          else:
              size = (image.shape[0],) + size
      else:
          raise ValueError(
              "Invalid input rank: expected rank 3 (single image) "
              "or rank 4 (batch of images). Received input with shape: "
              f"image.shape={image.shape}"
          )
      return np.array(
          jax.image.resize(
              image, size, method=interpolation, antialias=antialias
          )
      )
  
imports: import jax
  import numpy
  from keras.backend.numpy.core import convert_to_tensor
  from keras.utils.module_utils import scipy
label_desc: Use image node_id 1 for calling a global function with example usage: resize(image, size, interpolation, antialias, data_format) and returns: np