docstr: Evaluates the model.
  
  Args:
    use_trt: if true, use TRT INT8 mode for evaluation, which will perform
      real quantization. Otherwise use native TensorFlow which will perform
      simulated quantization. Ignored if is_training is True.
    batch_size: batch size.
    model_dir: where to save or load checkpoint.
    use_dynamic_shape: if true, then TF-TRT dynamic shape mode is enabled,
      otherwise disabled. Ignored if use_trt is false.
  
  Returns:
    The Estimator evaluation result.
code: def _Run(
      self, use_trt, batch_size, model_dir, use_dynamic_shape=False
  ):
      """Evaluates the model.
  
      Args:
        use_trt: if true, use TRT INT8 mode for evaluation, which will perform
          real quantization. Otherwise use native TensorFlow which will perform
          simulated quantization. Ignored if is_training is True.
        batch_size: batch size.
        model_dir: where to save or load checkpoint.
        use_dynamic_shape: if true, then TF-TRT dynamic shape mode is enabled,
          otherwise disabled. Ignored if use_trt is false.
  
      Returns:
        The Estimator evaluation result.
      """
      func = self._GetFunc(use_trt, model_dir, use_dynamic_shape)
      ds = _GetDataSet(batch_size)
  
      m = Accuracy()
      for example in ds:
          image, label = example[0], example[1]
          pred = func(image)
          m.update_state(math_ops.argmax(pred["output"], axis=1), label)
  
      return m.result().numpy()
  
imports: import os.path
  import tempfile
  import tensorflow_datasets
  from tensorflow.compiler.tf2tensorrt._pywrap_py_utils import is_tensorrt_enabled
  from tensorflow.core.protobuf import config_pb2
  from tensorflow.python.client import session
  from tensorflow.python.compiler.tensorrt import trt_convert
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.estimator.estimator import Estimator
  from tensorflow.python.estimator.model_fn import EstimatorSpec
  from tensorflow.python.estimator.model_fn import ModeKeys
  from tensorflow.python.estimator.run_config import RunConfig
  from tensorflow.python.framework import convert_to_constants
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import importer
  from tensorflow.python.framework import ops
  from tensorflow.python.framework import test_util
  from tensorflow.python.keras.metrics import Accuracy
  from tensorflow.python.layers import layers
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import gen_array_ops
  from tensorflow.python.ops import init_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import metrics
  from tensorflow.python.ops import nn
  from tensorflow.python.ops import variable_scope
  from tensorflow.python.ops.losses import losses
  from tensorflow.python.platform import test
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.saved_model import builder
  from tensorflow.python.saved_model import signature_constants
  from tensorflow.python.saved_model import tag_constants
  from tensorflow.python.saved_model import utils
  from tensorflow.python.saved_model import signature_def_utils
  from tensorflow.python.saved_model.load import load
  from tensorflow.python.summary import summary
  from tensorflow.python.training import saver
  from tensorflow.python.training.adam import AdamOptimizer
  from tensorflow.python.training.checkpoint_management import latest_checkpoint
  from tensorflow.python.training.training_util import get_global_step
label_desc: Use image node_id 3 for calling the MNISTTestV2 obj's underlying member method code with example usage: obj._Run(use_trt, batch_size, model_dir, use_dynamic_shape) and returns: m