class_docstr: Frozen-encoder coreference model.
class_docstr_tok: ["Frozen-encoder","coreference","model","."]
code: def train(
      self,
      train_inputs: list[JsonDict],
      validation_inputs: list[JsonDict],
      **train_kw,
  ):
      # Extract encoder features.
      logging.info("Train: extracting span representations...")
      train_edges = self._make_edges(train_inputs, show_progress=True)
      logging.info(
          "Train: %d edge targets from %d inputs.",
          len(train_edges),
          len(train_inputs),
      )
      logging.info("Validation: extracting span representations...")
      validation_edges = self._make_edges(
          validation_inputs, show_progress=True
      )
      logging.info(
          "Validation: %d edge targets from %d inputs.",
          len(validation_edges),
          len(validation_inputs),
      )
  
      # Train classifier layer.
      history = self.classifier.train(
          train_edges.examples, validation_edges.examples, **train_kw
      )
      return history
  
imports: import os
  from absl import logging
  from lit_nlp.api import dtypes
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.examples.coref import edge_predictor
  from lit_nlp.examples.coref import encoders
  from lit_nlp.examples.coref.datasets import winogender
  from lit_nlp.lib import utils
  import numpy
  import tqdm
label_desc: Use image node_id 4 for calling the FrozenEncoderCoref obj's underlying member method code with example usage: obj.train(train_inputs, validation_inputs) and returns: history