code: def get_coord_features(self, points, batchsize, rows, cols):
      if self.cpu_mode:
          coords = []
          for i in range(batchsize):
              norm_delimeter = (
                  1.0
                  if self.use_disks
                  else self.spatial_scale * self.norm_radius
              )
              coords.append(
                  self._get_dist_maps(
                      points[i].numpy().astype("float32"),
                      rows,
                      cols,
                      norm_delimeter,
                  )
              )
          coords = paddle.to_tensor(np.stack(coords, axis=0)).astype(
              "float32"
          )
      else:
          num_points = points.shape[1] // 2
          points = points.reshape([-1, points.shape[2]])
          points, points_order = paddle.split(points, [2, 1], axis=1)
          invalid_points = paddle.max(points, axis=1, keepdim=False) < 0
          row_array = paddle.arange(
              start=0, end=rows, step=1, dtype="float32"
          )
          col_array = paddle.arange(
              start=0, end=cols, step=1, dtype="float32"
          )
  
          coord_rows, coord_cols = paddle.meshgrid(row_array, col_array)
          coords = paddle.unsqueeze(
              paddle.stack([coord_rows, coord_cols], axis=0), axis=0
          ).tile([points.shape[0], 1, 1, 1])
  
          add_xy = (points * self.spatial_scale).reshape(
              [points.shape[0], points.shape[1], 1, 1]
          )
          coords = coords - add_xy
          if not self.use_disks:
              coords = coords / (self.norm_radius * self.spatial_scale)
  
          coords = coords * coords
          coords[:, 0] += coords[:, 1]
          coords = coords[:, :1]
          invalid_points = invalid_points.numpy()
  
          coords[invalid_points, :, :, :] = 1e6
          coords = coords.reshape([-1, num_points, 1, rows, cols])
          coords = paddle.min(coords, axis=1)
          coords = coords.reshape([-1, 2, rows, cols])
  
      if self.use_disks:
          coords = (
              coords <= (self.norm_radius * self.spatial_scale) ** 2
          ).astype("float32")
      else:
          coords = paddle.tanh(paddle.sqrt(coords) * 2)
      return coords
  
file_path: PaddleSeg/EISeg/eiseg/inference/predictor/ops.py
imports: import paddle
  import paddle.nn
  import numpy
label_desc: Use image node_id 2 for calling the DistMaps obj's underlying member method code with example usage: obj.get_coord_features(points, batchsize, rows, cols) and returns: coords