docstr:     Treat batch_size dimension and num dimension as one batch_size dimension
      (batch_size * num).
  :param input:  <batch_size, num, time_step, input_dim>
  :param num: the number of input time series data. For short term data, the num is 1.
  :return: the embedded of the input <batch_size, num, last_rnn_hid_size>
docstr_tok: ["Treat","batch_size","dimension","and","num","dimension","as","one","batch_size","dimension","(","batch_size","*","num",")",".",":","param","input",":","<","batch_size",",","num",",","time_step",",","input_dim",">",":","param","num",":","the","number","of","input","time","series","data",".","For","short","term","data",",","the","num","is","1.",":","return",":","the","embedded","of","the","input","<","batch_size",",","num",",","last_rnn_hid_size",">"]
code: def __encoder(self, input, num, name="Encoder", training=None):
      """
          Treat batch_size dimension and num dimension as one batch_size dimension
          (batch_size * num).
      :param input:  <batch_size, num, time_step, input_dim>
      :param num: the number of input time series data. For short term data, the num is 1.
      :return: the embedded of the input <batch_size, num, last_rnn_hid_size>
      """
      # input = Input(shape=(num, self.time_step, self.feature_num))
      batch_size_new = self.batch_size * num
      Tc = self.time_step - self.cnn_height + 1
  
      # CNN
      # reshaped input: (batch_size_new, time_step, feature_num, 1)
      reshaped_input = Lambda(
          lambda x: K.reshape(
              x,
              (-1, self.time_step, self.feature_num, 1),
          ),
          name=name + "reshape_cnn",
      )(input)
      # output: <batch_size_new, conv_out, 1, en_conv_hidden_size>
      cnn_out = Conv2D(
          filters=self.cnn_hid_size,
          kernel_size=(self.cnn_height, self.feature_num),
          padding="valid",
          kernel_initializer=TruncatedNormal(stddev=0.1),
          bias_initializer=Constant(0.1),
          activation="relu",
      )(reshaped_input)
      cnn_out = Dropout(self.cnn_dropout)(cnn_out, training=training)
  
      rnn_input = Lambda(
          lambda x: K.reshape(x, (-1, num, Tc, self.cnn_hid_size)),
      )(cnn_out)
  
      # use AttentionRNNWrapper
      rnn_cells = [
          GRUCell(h_size, activation="relu", dropout=self.rnn_dropout)
          for h_size in self.rnn_hid_sizes
      ]
  
      attention_rnn = AttentionRNNWrapper(
          RNN(rnn_cells), weight_initializer=TruncatedNormal(stddev=0.1)
      )
  
      outputs = []
      for i in range(num):
          input_i = rnn_input[:, i]
          # input_i = (batch, conv_hid_size, Tc)
          input_i = Permute(
              (2, 1), input_shape=[Tc, self.cnn_hid_size]
          )(input_i)
          # output = (batch, last_rnn_hid_size)
          output_i = attention_rnn(input_i, training=training)
          # output = (batch, 1, last_rnn_hid_size)
          output_i = Reshape((1, -1))(output_i)
          outputs.append(output_i)
      if len(outputs) > 1:
          output = Lambda(lambda x: K.concatenate(x, axis=1))(outputs)
      else:
          output = outputs[0]
      return output
  
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 5 for calling the MTNetKeras obj's underlying member method code with example usage: obj.__encoder(input, num, name, training) and returns: output