code: def fit_eval(
      self,
      data,
      validation_data=None,
      mc=False,
      metric=None,
      epochs=10,
      verbose=0,
      **config,
  ):
      x, y = data[0], data[1]
      feature_num, output_dim = self._check_input(x, y)
      self._add_config_attributes(
          config,
          epochs=epochs,
          mc=mc,
          metric=metric,
          feature_num=feature_num,
          output_dim=output_dim,
      )
      self.apply_config(config=self.config)
      processed_x, processed_validation_data = self._pre_processing(
          x, validation_data
      )
  
      # if model is not initialized, __build the model
      if self.model is None:
          st = time.time()
          self.build(self.config)
          end = time.time()
          if verbose == 1:
              print("Build model took {}s".format(end - st))
  
      st = time.time()
      hist = self.model.fit(
          processed_x,
          y,
          validation_data=processed_validation_data,
          batch_size=self.batch_size,
          epochs=self.epochs,
          verbose=verbose,
      )
  
      if verbose == 1:
          print("Fit model took {}s".format(time.time() - st))
  
      compiled_metric_names = self.model.metrics_names.copy()
      compiled_metric_names.remove("loss")
      hist_metric_name = tf.keras.metrics.get(self.metric).__name__
      if hist_metric_name in compiled_metric_names:
          metric_name = hist_metric_name
      elif metric in compiled_metric_names:
          metric_name = metric
      else:
          from bigdl.nano.utils.common import invalidInputError
  
          invalidInputError(
              False,
              f"Input metric in fit_eval should be one of the metrics that are "
              f"used to compile the model. Got metric value of {metric} and the "
              f"metrics in compile are {compiled_metric_names}",
          )
      if validation_data is None:
          result = hist.history.get(metric_name)[-1]
      else:
          result = hist.history.get("val_" + metric_name)[-1]
      return {self.metric: result}
  
imports: import pickle
  import numpy
  import time
  from tensorflow.keras.models import Model
  from tensorflow.keras.layers import *
  from tensorflow.keras.initializers import TruncatedNormal, Constant
  import tensorflow.keras.backend
  import tensorflow
  from bigdl.orca.automl.metrics import Evaluator
  from bigdl.orca.automl.model.abstract import BaseModel
label_desc: Use image node_id 10 for calling the MTNetKeras obj's underlying member method code with example usage: obj.fit_eval(data, validation_data, mc, metric, epochs, verbose) and returns: dict