docstr: Evaluate on x, y
  :param x: input
  :param y: target
  :param metric: a list of metrics in string format
  :param batch_size: Number of samples per batch.
         If unspecified, batch_size will default to 32.
  :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",":","param","batch_size",":","Number","of","samples","per","batch",".","If","unspecified",",","batch_size","will","default","to","32.",":","return",":","a","list","of","metric","evaluation","results"]
code: def evaluate(self, x, y, metrics=["mse"], batch_size=32):
      """
      Evaluate on x, y
      :param x: input
      :param y: target
      :param metric: a list of metrics in string format
      :param batch_size: Number of samples per batch.
             If unspecified, batch_size will default to 32.
      :return: a list of metric evaluation results
      """
      y_pred = self.predict(x, batch_size=batch_size)
      if y_pred.shape[1] == 1:
          aggregate = "mean"
      else:
          aggregate = None
      # y = np.squeeze(y, axis=2)
      return [
          Evaluator.evaluate(m, y, y_pred, aggregate=aggregate)
          for m in metrics
      ]
  
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 11 for calling the MTNetKeras obj's underlying member method code with example usage: obj.evaluate(x, y, metrics, batch_size) and returns: unknown