class_docstr: Twitter mention graphs related to major tennis tournaments from 2017.
  Nodes are Twitter accounts and edges are mentions between them.
  Each snapshot contains the graph induced by the most popular nodes
  of the original dataset. Node labels encode the number of mentions
  received in the original dataset for the next snapshot. Read more
  on the original Twitter data in the 'Temporal Walk Based Centrality Metric for Graph Streams' paper.
  
  Parameters
  ----------
  event_id : str
      Choose to load the mention network for Roland-Garros 2017 ("rg17") or USOpen 2017 ("uo17")
  N : int <= 1000
      Number of most popular nodes to load. By default N=1000. Each snapshot contains the graph induced by these nodes.
  feature_mode : str
      None : load raw degree and transitivity node features
      "encoded" : load onehot encoded degree and transitivity node features
      "diagonal" : set identity matrix as node features
  target_offset : int
      Set the snapshot offset for the node labels to be predicted. By default node labels for the next snapshot are predicted (target_offset=1).
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code: def __init__(
      self,
      event_id="rg17",
      N=None,
      feature_mode="encoded",
      target_offset=1,
  ):
      self.N = N
      self.target_offset = target_offset
      if event_id in ["rg17", "uo17"]:
          self.event_id = event_id
      else:
          raise ValueError(
              "Invalid 'event_id'! Choose 'rg17' or 'uo17' to load the Roland-Garros 2017 or the USOpen 2017 Twitter tennis dataset respectively."
          )
      if feature_mode in [None, "diagonal", "encoded"]:
          self.feature_mode = feature_mode
      else:
          raise ValueError(
              "Choose feature_mode from values [None, 'diagonal', 'encoded']."
          )
      self._read_web_data()
  
file_path: pytorch_geometric_temporal/torch_geometric_temporal/dataset/twitter_tennis.py
imports: import json
  import urllib
  import numpy
  from ..signal import DynamicGraphTemporalSignal
label_desc: Use image node_id 1 to create a new TwitterTennisDatasetLoader object from inherited base classes: object with example: obj = TwitterTennisDatasetLoader(event_id, N, feature_mode, target_offset)