class_docstr: Generic base class for the batched model.
  
  Subclass needs to implement predict_minibatch() and optionally
  max_minibatch_size().
docstr: Run prediction on a batch of inputs.
  
  Args:
    inputs: sequence of inputs, following model.input_spec()
  
  Returns:
    list of outputs, following model.output_spec()
docstr_tok: ["Run","prediction","on","a","batch","of","inputs",".","Args",":","inputs",":","sequence","of","inputs",",","following","model.input_spec","(",")","Returns",":","list","of","outputs",",","following","model.output_spec","(",")"]
code: def predict_minibatch(self, inputs: list[JsonDict]) -> list[JsonDict]:
      """Run prediction on a batch of inputs.
  
      Args:
        inputs: sequence of inputs, following model.input_spec()
  
      Returns:
        list of outputs, following model.output_spec()
      """
      pass
  
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 3 for calling the BatchedModel obj's underlying member method code with example usage: obj.predict_minibatch(inputs) without return types