code: def resampler(
      data: types.TensorLike,
      warp: types.TensorLike,
      name: Optional[str] = None,
  ) -> tf.Tensor:
      """Resamples input data at user defined coordinates.
  
      The resampler currently only supports bilinear interpolation of 2D data.
  
      Args:
        data: `Tensor` of shape `[batch_size, data_height, data_width,
          data_num_channels]` containing 2D data that will be resampled.
        warp: Tensor of minimum rank 2 containing the coordinates at
        which resampling will be performed. Since only bilinear
        interpolation is currently supported, the last dimension of the
        `warp` tensor must be 2, representing the `(x, y)` coordinate where
        `x` is the index for width and `y` is the index for height.
        name: Optional name of the op.
      Returns:
        Tensor of resampled values from `data`. The output tensor shape
        is determined by the shape of the warp tensor. For example, if `data`
        is of shape `[batch_size, data_height, data_width, data_num_channels]`
        and warp of shape `[batch_size, dim_0, ... , dim_n, 2]` the output will
        be of shape `[batch_size, dim_0, ... , dim_n, data_num_channels]`.
      Raises:
        ImportError: if the wrapper generated during compilation is not
        present when the function is called.
      """
      with tf.name_scope(name or "resampler"):
          data_tensor = tf.convert_to_tensor(data, name="data")
          warp_tensor = tf.convert_to_tensor(warp, name="warp")
          return _resampler_so.ops.addons_resampler(
              data_tensor, warp_tensor
          )
  
imports: import tensorflow
  from tensorflow_addons.utils import types
  from tensorflow_addons.utils.resource_loader import LazySO
  from typing import Optional
label_desc: Use image node_id 1 for calling a global function with example usage: resampler(data, warp, name) and returns: _resampler_so