class_docstr: Evaluation output (always contains labels), to be used to compute metrics.
  
  Parameters:
      predictions (`np.ndarray`): Predictions of the model.
      label_ids (`np.ndarray`): Targets to be matched.
      inputs (`np.ndarray`, *optional*):
class_docstr_tok: ["Evaluation","output","(","always","contains","labels",")",",","to","be","used","to","compute","metrics",".","Parameters",":","predictions","(","`","np.ndarray","`",")",":","Predictions","of","the","model",".","label_ids","(","`","np.ndarray","`",")",":","Targets","to","be","matched",".","inputs","(","`","np.ndarray","`",",","*","optional","*",")",":"]
code: def __iter__(self):
      if self.inputs is not None:
          return iter((self.predictions, self.label_ids, self.inputs))
      else:
          return iter((self.predictions, self.label_ids))
  
imports: import copy
  import functools
  import gc
  import inspect
  import os
  import random
  import re
  import threading
  import time
  from typing import Any, Dict, List, NamedTuple, Optional, Tuple, Union
  import numpy
  from .utils import ExplicitEnum, is_psutil_available, is_tf_available, is_torch_available, is_torch_cuda_available, is_torch_mps_available, is_torch_npu_available, is_torch_tpu_available, is_torch_xpu_available, requires_backends
label_desc: Use image node_id 2 for calling the EvalPrediction obj's underlying member method code with example usage: obj.__iter__() and returns: iter, iter