class_docstr: Handles evaluation of a given model over a specified dataset.
docstr: :param data_loader: The PyTorch DataLoader that will return batches of data from the evaluation dataset
  :type data_loader: DataLoader
  :param label_maps:
  :param device: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
  :param metrics: The list of metrics which need to be computed, one for each prediction head.
  :param metrics: list
  :param report: Whether an eval report should be generated (e.g. classification report per class).
  :type report: bool
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code: def __init__(self, data_loader, tasks, device, report=True):
      """
      :param data_loader: The PyTorch DataLoader that will return batches of data from the evaluation dataset
      :type data_loader: DataLoader
      :param label_maps:
      :param device: The device on which the tensors should be processed. Choose from "cpu" and "cuda".
      :param metrics: The list of metrics which need to be computed, one for each prediction head.
      :param metrics: list
      :param report: Whether an eval report should be generated (e.g. classification report per class).
      :type report: bool
      """
  
      self.data_loader = data_loader
      self.tasks = tasks
      self.device = device
      self.report = report
  
file_path: FARM/farm/eval.py
imports: from tqdm import tqdm
  import torch
  import numbers
  import logging
  import numpy
  from torch.utils.data import DataLoader
  from farm.evaluation.metrics import compute_metrics, compute_report_metrics
  from farm.utils import to_numpy
  from farm.utils import MLFlowLogger
  from farm.modeling.adaptive_model import AdaptiveModel
  from farm.modeling.biadaptive_model import BiAdaptiveModel
  from farm.visual.ascii.images import BUSH_SEP
label_desc: Use image node_id 1 to create a new Evaluator object  with example: obj = Evaluator(data_loader, tasks, device, report)