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, metric=["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)
      # y = np.squeeze(y, axis=2)
      if self.target_col_num == 1:
          return [Evaluator.evaluate(m, y, y_pred) for m in metric]
      else:
          return [
              np.array(
                  [
                      Evaluator.evaluate(m, y[:, i, :], y_pred[:, i, :])
                      for i in range(self.future_seq_len)
                  ]
              )
              for m in metric
          ]
  
imports: import pickle
  import numpy
  from tensorflow.keras.models import Model
  from tensorflow.keras.layers import Input, LSTM, Dense
  import tensorflow.keras
  from bigdl.orca.automl.model.abstract import BaseModel
  from bigdl.orca.automl.metrics import Evaluator
label_desc: Use image node_id 11 for calling the LSTMSeq2Seq obj's underlying member method code with example usage: obj.evaluate(x, y, metric) and returns: unknown, unknown