code: def _setUp_td_loss(
      self, gae_lambda=1, sequence_length=4, batch_size=2
  ):
      t, b = sequence_length, batch_size
      self._state_values = tf.placeholder(tf.float32, shape=(t, b))
      self._rewards = tf.placeholder(tf.float32, shape=(t, b))
      self._pcontinues = tf.placeholder(tf.float32, shape=(t, b))
      self._bootstrap_value = tf.placeholder(tf.float32, shape=(b,))
      loss, (td, discounted_returns) = value_ops.td_lambda(
          state_values=self._state_values,
          rewards=self._rewards,
          pcontinues=self._pcontinues,
          bootstrap_value=self._bootstrap_value,
          lambda_=gae_lambda,
      )
      self._loss = loss
      self._temporal_differences = td
      self._discounted_returns = discounted_returns
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow.compat.v1
  import tree
  from trfl import value_ops
label_desc: Use image node_id 1 for calling the TDLambdaTest obj's underlying member method code with example usage: obj._setUp_td_loss(gae_lambda, sequence_length, batch_size) without return types