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 Reshape, Dense, Dropout, Add, Permute, RNN, GRUCell
  from tensorflow.keras.layers import Input, InputSpec, Wrapper, Lambda, Conv2D, multiply, Softmax
  from tensorflow.keras.initializers import TruncatedNormal, Constant
  import tensorflow.keras.backend
  import tensorflow
  from bigdl.chronos.metric.forecast_metrics import Evaluator
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