class_docstr: A base class for keras optimized model.
class_docstr_tok: ["A","base","class","for","keras","optimized","model","."]
docstr: Same to keras model's `evaluate` method, except that it runs in eager mode.
docstr_tok: ["Same","to","keras","model","'s","`","evaluate","`","method",",","except","that","it","runs","in","eager","mode","."]
code: def evaluate(
      self,
      x=None,
      y=None,
      batch_size=None,
      verbose="auto",
      sample_weight=None,
      steps=None,
      callbacks=None,
      max_queue_size=10,
      workers=1,
      use_multiprocessing=False,
      return_dict=False,
      **kwargs,
  ):
      """Same to keras model's `evaluate` method, except that it runs in eager mode."""
      # todo: we should implement our evaluate method, because tf's predict will convert
      # numpy to tensor, then convert tensor to numpy when using ov/onnx optimized model
      self.run_eagerly = True
      return super().evaluate(
          x=x,
          y=y,
          batch_size=batch_size,
          verbose=verbose,
          sample_weight=sample_weight,
          steps=steps,
          callbacks=callbacks,
          max_queue_size=max_queue_size,
          workers=workers,
          use_multiprocessing=use_multiprocessing,
          return_dict=return_dict,
          **kwargs,
      )
  
file_path: BigDL/python/nano/src/bigdl/nano/tf/model.py
imports: from pathlib import Path
  from typing import Sequence, Any, Union, Dict
  import yaml
  import tensorflow
  from bigdl.nano.utils.common import invalidInputError
label_desc: Use image node_id 7 for calling the KerasOptimizedModel obj's underlying member method code with example usage: obj.evaluate(x, y, batch_size, verbose, sample_weight, steps, callbacks, max_queue_size, workers, use_multiprocessing, return_dict) and returns: super