docstr: Evaluate on x, y
  :param x: input
  :param y: target
  :param metric: a list of metrics in string format
  :return: a list of metric evaluation results
docstr_tok: ["Evaluate","on","x",",","y",":","param","x",":","input",":","param","y",":","target",":","param","metric",":","a","list","of","metrics","in","string","format",":","return",":","a","list","of","metric","evaluation","results"]
code: def evaluate(self, x, y, metrics=["mse"]):
      """
      Evaluate on x, y
      :param x: input
      :param y: target
      :param metric: a list of metrics in string format
      :return: a list of metric evaluation results
      """
      y_pred = self.predict(x)
      if y_pred.shape[1] == 1:
          multioutput = "uniform_average"
      else:
          multioutput = "raw_values"
      # y = np.squeeze(y, axis=2)
      return [
          Evaluator.evaluate(m, y, y_pred, multioutput=multioutput)
          for m in metrics
      ]
  
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 zoo.orca.automl.metrics import Evaluator
  from zoo.orca.automl.model.abstract import BaseModel
label_desc: Use image node_id 11 for calling the MTNetKeras obj's underlying member method code with example usage: obj.evaluate(x, y, metrics) and returns: unknown