class_docstr: Creates edges based on atom positions :obj:`pos` to all points within
  the cutoff distance.
  
  Args:
      cutoff (float, optional): Cutoff distance for interatomic interactions.
          (default: :obj:`10.0`)
      max_num_neighbors (int, optional): The maximum number of neighbors to
          collect for each node within the :attr:`cutoff` distance with the
          default interaction graph method.
          (default: :obj:`32`)
class_docstr_tok: ["Creates","edges","based","on","atom","positions",":","obj",":","`","pos","`","to","all","points","within","the","cutoff","distance",".","Args",":","cutoff","(","float",",","optional",")",":","Cutoff","distance","for","interatomic","interactions",".","(","default",":",":","obj",":","`","10.0","`",")","max_num_neighbors","(","int",",","optional",")",":","The","maximum","number","of","neighbors","to","collect","for","each","node","within","the",":","attr",":","`","cutoff","`","distance","with","the","default","interaction","graph","method",".","(","default",":",":","obj",":","`","32","`",")"]
code: def __init__(self, cutoff: float = 10.0, max_num_neighbors: int = 32):
      super().__init__()
      self.cutoff = cutoff
      self.max_num_neighbors = max_num_neighbors
  
imports: import os
  import os.path
  import warnings
  from math import pi
  from typing import Callable, Dict, Optional, Tuple
  import numpy
  import torch
  import torch.nn.functional
  from torch import Tensor
  from torch.nn import Embedding, Linear, ModuleList, Sequential
  from torch_geometric.data import Dataset, download_url, extract_zip
  from torch_geometric.nn import MessagePassing, SumAggregation, radius_graph
  from torch_geometric.nn.resolver import aggregation_resolver
  from torch_geometric.typing import OptTensor
label_desc: Use image node_id 1 to create a new RadiusInteractionGraph object from inherited base classes: torch.nn with example: obj = RadiusInteractionGraph(cutoff, max_num_neighbors)