class_docstr: A non-distributed Dataset or iterator in eager execution.
code: def evaluate(
      self,
      model,
      x=None,
      y=None,
      batch_size=None,
      verbose=1,
      sample_weight=None,
      steps=None,
      callbacks=None,
      **kwargs,
  ):
      model._validate_or_infer_batch_size(batch_size, steps, x)
      # Make sure that y, sample_weights, validation_split are not passed.
      training_utils_v1.validate_dataset_input(x, y, sample_weight)
      return evaluate_generator(
          model,
          x,
          steps=steps,
          verbose=verbose,
          workers=0,
          callbacks=callbacks,
      )
  
imports: import functools
  import math
  import numpy
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import callbacks
  from tensorflow.python.keras.engine import training_utils
  from tensorflow.python.keras.engine import training_utils_v1
  from tensorflow.python.keras.utils import data_utils
  from tensorflow.python.keras.utils import generic_utils
  from tensorflow.python.keras.utils.mode_keys import ModeKeys
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.types import data
  from tensorflow.python.util import nest
label_desc: Use image node_id 2 for calling the EagerDatasetOrIteratorTrainingLoop obj's underlying member method code with example usage: obj.evaluate(model, x, y, batch_size, verbose, sample_weight, steps, callbacks) and returns: evaluate_generator