docstr: Predict using a the trained model after HPO(Hyper Parameter Optimization).
  
  :param data: a numpy ndarray x, where x's shape is (num_samples, lookback, feature_dim)
         where lookback and feature_dim should be the same as past_seq_len and
         input_feature_num.
  :param batch_size: predict batch size. The value will not affect predict
         result but will affect resources cost(e.g. memory and time). The value
         defaults to 32.
  
  :return: A numpy array with shape (num_samples, horizon, target_dim).
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code: def predict(self, data, batch_size=32):
      """
      Predict using a the trained model after HPO(Hyper Parameter Optimization).
  
      :param data: a numpy ndarray x, where x's shape is (num_samples, lookback, feature_dim)
             where lookback and feature_dim should be the same as past_seq_len and
             input_feature_num.
      :param batch_size: predict batch size. The value will not affect predict
             result but will affect resources cost(e.g. memory and time). The value
             defaults to 32.
  
      :return: A numpy array with shape (num_samples, horizon, target_dim).
      """
      if self.best_model is None:
          raise RuntimeError(
              "You must call fit or load first before calling predict!"
          )
      return self.best_model.predict(data, batch_size=batch_size)
  
file_path: analytics-zoo/pyzoo/zoo/chronos/autots/model/base_automodel.py
imports: import os
  import json
label_desc: Use image node_id 3 for calling the BasePytorchAutomodel obj's underlying member method code with example usage: obj.predict(data, batch_size) and returns: self