class_docstr: Coref model for a single edge. Compatible with EdgeFeaturesDataset.
class_docstr_tok: ["Coref","model","for","a","single","edge",".","Compatible","with","EdgeFeaturesDataset","."]
docstr: Extract features from input records and return as dict of columns.
docstr_tok: ["Extract","features","from","input","records","and","return","as","dict","of","columns","."]
code: def _make_feature_columns(self, inputs: Iterable[JsonDict]):
      """Extract features from input records and return as dict of columns."""
      return {
          "span1_embs": tf.constant(
              [ex["span1_embs"] for ex in inputs], dtype=tf.float32
          ),
          "span2_embs": tf.constant(
              [ex["span2_embs"] for ex in inputs], dtype=tf.float32
          ),
      }
  
imports: import os
  from collections.abc import Iterable
  from typing import Optional
  from absl import logging
  from lit_nlp.api import dataset
  from lit_nlp.api import dtypes
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.lib import utils
  import numpy
  import tensorflow
label_desc: Use image node_id 3 for calling the SingleEdgePredictor obj's underlying member method code with example usage: obj._make_feature_columns(inputs) and returns: dict