class_docstr: Simple evaluator implementation. Uses the vanilla evaluation code.
  
  # Parameters
  
  batch_postprocessor: `Postprocessor`, optional (default=`SimplePostprocessor`)
      The postprocessor to use for turning both the batches and the outputs
      of the model into human readable data.
  
  cuda_device : `Union[int, torch.device]`, optional (default=`-1`)
      The cuda device to use for this evaluation.  The model is assumed to
       already be using this device; this parameter is only used for moving
       the input data to the correct device.
  
  postprocessor_fn_name: `str`, optional (default=`"make_output_human_readable"`)
      Function name of the model's postprocessing function.
code: def __init__(
      self,
      batch_serializer: Optional[Serializer] = None,
      cuda_device: Union[int, torch.device] = -1,
      postprocessor_fn_name: str = "make_output_human_readable",
  ):
      super(SimpleEvaluator, self).__init__(
          batch_serializer, cuda_device, postprocessor_fn_name
      )
  
imports: from typing import Union, Dict, Any, Optional
  from os import PathLike
  from pathlib import Path
  import torch
  import logging
  from allennlp.common.checks import check_for_gpu
  from allennlp.common.tqdm import Tqdm
  from allennlp.common.util import dump_metrics, int_to_device
  from allennlp.nn import util
  from allennlp.common import Registrable
  from allennlp.models import Model
  from allennlp.data import DataLoader
  from allennlp.evaluation.serializers.serializers import Serializer, SimpleSerializer
label_desc: Use image node_id 1 to create a new SimpleEvaluator object from inherited base classes: Evaluator with example: obj = SimpleEvaluator(batch_serializer, cuda_device, postprocessor_fn_name)