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 __init__(
      self,
      r: float,
      loop: bool = False,
      max_num_neighbors: int = 32,
      flow: str = "source_to_target",
      num_workers: int = 1,
  ) -> None:
      self.r = r
      self.loop = loop
      self.max_num_neighbors = max_num_neighbors
      self.flow = flow
      self.num_workers = num_workers
  
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 1 to create a new RadiusGraph object from inherited base classes: BaseTransform with example: obj = RadiusGraph(r, loop, max_num_neighbors, flow, num_workers)