class_docstr: TFTrainer is a simple but feature-complete training and eval loop for TensorFlow, optimized for 🤗 Transformers.
  
  Args:
      model ([`TFPreTrainedModel`]):
          The model to train, evaluate or use for predictions.
      args ([`TFTrainingArguments`]):
          The arguments to tweak training.
      train_dataset ([`~tf.data.Dataset`], *optional*):
          The dataset to use for training. The dataset should yield tuples of `(features, labels)` where `features`
          is a dict of input features and `labels` is the labels. If `labels` is a tensor, the loss is calculated by
          the model by calling `model(features, labels=labels)`. If `labels` is a dict, such as when using a
          QuestionAnswering head model with multiple targets, the loss is instead calculated by calling
          `model(features, **labels)`.
      eval_dataset ([`~tf.data.Dataset`], *optional*):
          The dataset to use for evaluation. The dataset should yield tuples of `(features, labels)` where `features`
          is a dict of input features and `labels` is the labels. If `labels` is a tensor, the loss is calculated by
          the model by calling `model(features, labels=labels)`. If `labels` is a dict, such as when using a
          QuestionAnswering head model with multiple targets, the loss is instead calculated by calling
          `model(features, **labels)`.
      compute_metrics (`Callable[[EvalPrediction], Dict]`, *optional*):
          The function that will be used to compute metrics at evaluation. Must take a [`EvalPrediction`] and return
          a dictionary string to metric values.
      tb_writer (`tf.summary.SummaryWriter`, *optional*):
          Object to write to TensorBoard.
      optimizers (`Tuple[tf.keras.optimizers.Optimizer, tf.keras.optimizers.schedules.LearningRateSchedule]`, *optional*):
          A tuple containing the optimizer and the scheduler to use. The optimizer default to an instance of
          [`tf.keras.optimizers.Adam`] if `args.weight_decay_rate` is 0 else an instance of [`AdamWeightDecay`]. The
          scheduler will default to an instance of [`tf.keras.optimizers.schedules.PolynomialDecay`] if
          `args.num_warmup_steps` is 0 else an instance of [`WarmUp`].
docstr: Run evaluation and returns metrics.
  
  The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
  (pass it to the init `compute_metrics` argument).
  
  Args:
      eval_dataset ([`~tf.data.Dataset`], *optional*):
          Pass a dataset if you wish to override `self.eval_dataset`. The dataset should yield tuples of
          `(features, labels)` where `features` is a dict of input features and `labels` is the labels. If
          `labels` is a tensor, the loss is calculated by the model by calling `model(features, labels=labels)`.
          If `labels` is a dict, such as when using a QuestionAnswering head model with multiple targets, the
          loss is instead calculated by calling `model(features, **labels)`.
  
  Returns:
      A dictionary containing the evaluation loss and the potential metrics computed from the predictions.
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code: def evaluate(
      self, eval_dataset: Optional[tf.data.Dataset] = None
  ) -> Dict[str, float]:
      """
      Run evaluation and returns metrics.
  
      The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
      (pass it to the init `compute_metrics` argument).
  
      Args:
          eval_dataset ([`~tf.data.Dataset`], *optional*):
              Pass a dataset if you wish to override `self.eval_dataset`. The dataset should yield tuples of
              `(features, labels)` where `features` is a dict of input features and `labels` is the labels. If
              `labels` is a tensor, the loss is calculated by the model by calling `model(features, labels=labels)`.
              If `labels` is a dict, such as when using a QuestionAnswering head model with multiple targets, the
              loss is instead calculated by calling `model(features, **labels)`.
  
      Returns:
          A dictionary containing the evaluation loss and the potential metrics computed from the predictions.
      """
      eval_ds, steps, num_examples = self.get_eval_tfdataset(
          eval_dataset
      )
  
      output = self.prediction_loop(
          eval_ds, steps, num_examples, description="Evaluation"
      )
      logs = {**output.metrics}
      logs["epoch"] = self.epoch_logging
  
      self.log(logs)
  
      return output.metrics
  
imports: import datetime
  import math
  import os
  import warnings
  from typing import Callable, Dict, Optional, Tuple
  from .utils import ENV_VARS_TRUE_VALUES
  from .integrations import is_comet_available, is_wandb_available
  import numpy
  import tensorflow
  from tensorflow.python.distribute.values import PerReplica
  from .modeling_tf_utils import TFPreTrainedModel
  from .optimization_tf import GradientAccumulator, create_optimizer
  from .trainer_utils import PREFIX_CHECKPOINT_DIR, EvalPrediction, IntervalStrategy, PredictionOutput, enable_full_determinism, set_seed
  from .training_args_tf import TFTrainingArguments
  from .utils import logging
label_desc: Use image node_id 10 for calling the TFTrainer obj's underlying member method code with example usage: obj.evaluate(eval_dataset) and returns: output