code: def batch_evaluation(
      self,
      dataset,
      collect_predictions=False,
      collect_logits=False,
      dataset_name=None,
  ):
      raise NotImplementedError()
  
imports: import logging
  import os
  import sys
  from abc import ABC, abstractmethod
  from collections import defaultdict, OrderedDict
  from pprint import pformat
  from typing import Dict, List, Optional, Type
  import numpy
  import pandas
  import psutil
  import torch
  from torch import nn
  from ludwig.constants import COMBINED, LAST_HIDDEN, LOGITS, MODEL_ECD, MODEL_GBM, MODEL_LLM
  from ludwig.data.dataset.base import Dataset
  from ludwig.data.utils import convert_to_dict
  from ludwig.distributed.base import DistributedStrategy, LocalStrategy
  from ludwig.globals import is_progressbar_disabled, PREDICTIONS_PARQUET_FILE_NAME, TEST_STATISTICS_FILE_NAME
  from ludwig.models.base import BaseModel
  from ludwig.progress_bar import LudwigProgressBar
  from ludwig.utils.data_utils import save_csv, save_json
  from ludwig.utils.dataframe_utils import from_numpy_dataset
  from ludwig.utils.print_utils import repr_ordered_dict
  from ludwig.utils.registry import Registry
  from ludwig.utils.strings_utils import make_safe_filename
  from ludwig.utils.torch_utils import get_torch_device
label_desc: Use image node_id 3 for calling the BasePredictor obj's underlying member method code with example usage: obj.batch_evaluation(dataset, collect_predictions, collect_logits, dataset_name) without return types