class_docstr: Creates edges based on node positions :obj:`data.pos` to all points
  within a given distance (functional name: :obj:`radius_graph`).
  
  Args:
      r (float): The distance.
      loop (bool, optional): If :obj:`True`, the graph will contain
          self-loops. (default: :obj:`False`)
      max_num_neighbors (int, optional): The maximum number of neighbors to
          return for each element in :obj:`y`.
          This flag is only needed for CUDA tensors. (default: :obj:`32`)
      flow (str, optional): The flow direction when using in combination with
          message passing (:obj:`"source_to_target"` or
          :obj:`"target_to_source"`). (default: :obj:`"source_to_target"`)
      num_workers (int): Number of workers to use for computation. Has no
          effect in case :obj:`batch` is not :obj:`None`, or the input lies
          on the GPU. (default: :obj:`1`)
code: def forward(self, data: Data) -> Data:
      assert data.pos is not None
  
      data.edge_index = torch_geometric.nn.radius_graph(
          data.pos,
          self.r,
          data.batch,
          self.loop,
          max_num_neighbors=self.max_num_neighbors,
          flow=self.flow,
          num_workers=self.num_workers,
      )
      data.edge_attr = None
  
      return data
  
file_path: pytorch_geometric/torch_geometric/transforms/radius_graph.py
imports: import torch_geometric
  from torch_geometric.data import Data
  from torch_geometric.data.datapipes import functional_transform
  from torch_geometric.transforms import BaseTransform
label_desc: Use image node_id 2 for calling the RadiusGraph obj's underlying member method code with example usage: obj.forward(data) and returns: data