import tensorflow as tf

     A tuple containing:

     - Input tensor of the model.
     - Prediction tensor of the model.
     - Output class tensor of the model.
    """
    # Weights and biases for output softmax layer.
    out_weights = tf.Variable(
        tf.random_normal([self.num_units, self.n_classes]))
    out_bias = tf.Variable(tf.random_normal([self.n_classes]))

    # input image placeholder
    x = tf.placeholder(
        "float", [None, self.time_steps, self.n_input], name="INPUT_IMAGE")

    # x is shaped [batch_size,time_steps,num_inputs]
    if is_dynamic_rnn:
      lstm_input = tf.transpose(x, perm=[1, 0, 2])
      outputs, _ = tf.lite.experimental.nn.dynamic_rnn(
          lstm_layer, lstm_input, dtype="float32")
      outputs = tf.unstack(outputs, axis=0)
    else:
      lstm_input = tf.unstack(x, self.time_steps, 1)
      outputs, _ = tf.nn.static_rnn(lstm_layer, lstm_input, dtype="float32")
