code: def build_network(self, input_shape, **kwargs):
      """Construct a compiled, un-trained, keras model that is ready for training.
  
      Parameters
      ----------
      input_shape : int or tuple
          The shape of the data fed into the input layer. It should either
          have dimensions of (m, d) or m. In case an int is passed,
          1 is appended for d.
  
      Returns
      -------
      output : a compiled Keras Model
      """
      from tensorflow import keras
  
      if isinstance(input_shape, int):
          input_layer = keras.layers.Input((input_shape, 1))
      elif isinstance(input_shape, tuple):
          if len(input_shape) == 2:
              input_layer = keras.layers.Input(input_shape)
          elif len(input_shape) == 1:
              input_layer = keras.layers.Input((*input_shape, 1))
          else:
              raise ValueError(
                  "If `input_shape` is a tuple, it must either be "
                  f"of length 1 or 2. Found length of {len(input_shape)}"
              )
      else:
          raise TypeError(
              "`input_shape` should either be of type int or tuple. "
              f"But found the type to be: {type(input_shape)}"
          )
  
      output_layer = keras.layers.SimpleRNN(
          units=self.units,
          input_shape=input_layer.shape,
          activation="linear",
          use_bias=False,
          kernel_initializer="glorot_uniform",
          recurrent_initializer="orthogonal",
          bias_initializer="zeros",
          dropout=0.0,
          recurrent_dropout=0.0,
      )(input_layer)
  
      return input_layer, output_layer
  
imports: from sktime.networks.base import BaseDeepNetwork
label_desc: Use image node_id 2 for calling the RNNNetwork obj's underlying member method code with example usage: obj.build_network(input_shape) and returns: input_layer,  output_layer