class_docstr: Calculates anaphoricity scores by passing the inputs into a FFNN 
code: def __init__(self, in_features: int, config: Config):
      super().__init__()
      hidden_size = config.hidden_size
      if not config.n_hidden_layers:
          hidden_size = in_features
      layers = []
      for i in range(config.n_hidden_layers):
          layers.extend(
              [
                  torch.nn.Linear(
                      hidden_size if i else in_features, hidden_size
                  ),
                  torch.nn.LeakyReLU(),
                  torch.nn.Dropout(config.dropout_rate),
              ]
          )
      self.hidden = torch.nn.Sequential(*layers)
      self.out = torch.nn.Linear(hidden_size, out_features=1)
  
file_path: stanza/stanza/models/coref/anaphoricity_scorer.py
imports: import torch
  from stanza.models.coref import utils
  from stanza.models.coref.config import Config
label_desc: Use image node_id 1 to create a new AnaphoricityScorer object from inherited base classes: torch.nn with example: obj = AnaphoricityScorer(in_features, config)