class_docstr: GLUE benchmark model, using Keras/TF2 and Huggingface Transformers.
  
  This is a general-purpose classification or regression model. It works for
  one- or two-segment input, and predicts either a multiclass label or
  a regression score. See GlueModelConfig for available options.
  
  This implements the LIT API for inference (e.g. input_spec(), output_spec(),
  and predict()), but also provides a train() method to run fine-tuning.
  
  This is a full-featured implementation, which includes embeddings, attention,
  gradients, as well as support for the different input and output types above.
  For a more minimal example, see ../simple_tf2_demo.py.
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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."""
      encoded_input = self._preprocess(inputs)
      if self.is_regression:
          labels = tf.constant(
              [ex[self.config.label_name] for ex in inputs],
              dtype=tf.float32,
          )
      else:
          labels = tf.constant(
              [
                  self.config.labels.index(ex[self.config.label_name])
                  for ex in inputs
              ],
              dtype=tf.int64,
          )
      # encoded_input is actually a transformers.BatchEncoding
      # object, which tf.data.Dataset doesn't like. Convert to a regular dict.
      return tf.data.Dataset.from_tensor_slices(
          (dict(encoded_input), labels)
      )
  
imports: import os
  from collections.abc import Iterable, Sequence
  import re
  import threading
  from typing import Any, Optional
  import attr
  from lit_nlp.api import model
  from lit_nlp.api import types
  from lit_nlp.examples.models import model_utils
  from lit_nlp.lib import file_cache
  from lit_nlp.lib import utils
  import numpy
  import tensorflow
  import transformers
label_desc: Use image node_id 7 for calling the GlueModel obj's underlying member method code with example usage: obj._make_dataset(inputs) and returns: tf