class_docstr: Coref model for a single edge. Compatible with EdgeFeaturesDataset.
class_docstr_tok: ["Coref","model","for","a","single","edge",".","Compatible","with","EdgeFeaturesDataset","."]
docstr: Make a tf.data.Dataset from inputs in LIT format.
docstr_tok: ["Make","a","tf.data.Dataset","from","inputs","in","LIT","format","."]
code: def _make_dataset(
      self, inputs: Iterable[JsonDict]
  ) -> tf.data.Dataset:
      """Make a tf.data.Dataset from inputs in LIT format."""
      # Convert to feature columns for tf.data APIs
      features = self._make_feature_columns(inputs)
      labels = tf.constant(
          [ex["label"] for ex in inputs], dtype=tf.float32
      )
      return tf.data.Dataset.from_tensor_slices((features, labels))
  
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 4 for calling the SingleEdgePredictor obj's underlying member method code with example usage: obj._make_dataset(inputs) and returns: tf