class_docstr: Loads a saved AdaptiveModel/ONNXAdaptiveModel from disk and runs it in inference mode. Can be used for a
  model with prediction head (down-stream predictions) and without (using LM as embedder).
  
  Example usage:
  
  .. code-block:: python
  
     # down-stream inference
     basic_texts = [
         {"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"},
         {"text": "Martin Müller spielt Handball in Berlin"},
     ]
     model = Inferencer.load(your_model_dir)
     model.inference_from_dicts(dicts=basic_texts)
     # LM embeddings
     model = Inferencer.load(your_model_dir, extraction_strategy="cls_token", extraction_layer=-1)
     model.inference_from_dicts(dicts=basic_texts)
docstr: Convert ONE chunk of data (i.e. dictionaries) into ONE pytorch dataset.
  This is usually executed in one of many parallel processes.
  The resulting datasets of the processes are merged together afterwards
docstr_tok: ["Convert","ONE","chunk","of","data","(","i.e",".","dictionaries",")","into","ONE","pytorch","dataset",".","This","is","usually","executed","in","one","of","many","parallel","processes",".","The","resulting","datasets","of","the","processes","are","merged","together","afterwards"]
code: def _create_datasets_chunkwise(cls, chunk, processor):
      """Convert ONE chunk of data (i.e. dictionaries) into ONE pytorch dataset.
      This is usually executed in one of many parallel processes.
      The resulting datasets of the processes are merged together afterwards
      """
      dicts = [d[1] for d in chunk]
      indices = [d[0] for d in chunk]
      (
          dataset,
          tensor_names,
          problematic_sample_ids,
          baskets,
      ) = processor.dataset_from_dicts(
          dicts, indices, return_baskets=True
      )
      return dataset, tensor_names, problematic_sample_ids, baskets
  
imports: import logging
  import multiprocessing
  import os
  from functools import partial
  import warnings
  import torch
  from torch.utils.data.sampler import SequentialSampler
  from tqdm import tqdm
  from typing import Generator, List, Union
  from farm.data_handler.dataloader import NamedDataLoader
  from farm.data_handler.processor import Processor, InferenceProcessor, NaturalQuestionsProcessor
  from farm.data_handler.utils import grouper
  from farm.data_handler.inputs import QAInput
  from farm.modeling.adaptive_model import AdaptiveModel, BaseAdaptiveModel, ONNXAdaptiveModel
  from farm.modeling.optimization import optimize_model
  from farm.utils import initialize_device_settings, MLFlowLogger
  from farm.utils import set_all_seeds, calc_chunksize, log_ascii_workers, Benchmarker
  from farm.modeling.predictions import QAPred
label_desc: Use image node_id 10 for calling the Inferencer obj's underlying member method code with example usage: obj._create_datasets_chunkwise(cls, chunk, processor) and returns: dataset,  tensor_names,  problematic_sample_ids,  baskets