class_docstr: Generic base class for remotely-hosted models.
  
  Implements concurrent request batching; subclass need only implement
  predict_minibatch() and max_minibatch_size().
  
  If subclass overrides __init__, it should be sure to call super().__init__()
  to set up the threadpool.
docstr: Remote models can handle concurrent predictions by default.
docstr_tok: ["Remote","models","can","handle","concurrent","predictions","by","default","."]
code: def supports_concurrent_predictions(self):
      """Remote models can handle concurrent predictions by default."""
      return True
  
imports: import abc
  from collections.abc import Iterable, Iterator
  import inspect
  import itertools
  import multiprocessing
  from typing import Optional, Union
  from absl import logging
  from lit_nlp.api import dataset
  from lit_nlp.api import types
  from lit_nlp.lib import utils
  import numpy
label_desc: Use image node_id 4 for calling the BatchedRemoteModel obj's underlying member method code with example usage: obj.supports_concurrent_predictions() and returns: True