code: def test_gradient_paths(self):
      time_step = 3
      input_dim = 2
      output_dim = 4
      obs_inputs = np.full((self.batch_size, time_step, input_dim), 1.0)
      obs_input = np.full((self.batch_size, input_dim), 1.0)
  
      input_var = tf.compat.v1.placeholder(
          tf.float32, shape=(None, None, input_dim), name="input"
      )
      step_input_var = tf.compat.v1.placeholder(
          tf.float32, shape=(None, input_dim), name="step_input"
      )
      output_nonlinearity = tf.keras.layers.Dense(
          units=output_dim,
          activation=None,
          kernel_initializer=tf.constant_initializer(1),
      )
      with tf.compat.v1.variable_scope("GRU"):
          self.gru = gru(
              all_input_var=input_var,
              name="gru",
              gru_cell=self.gru_cell,
              step_input_var=step_input_var,
              step_hidden_var=self.step_hidden_var,
              output_nonlinearity_layer=output_nonlinearity,
          )
  
      self.sess.run(tf.compat.v1.global_variables_initializer())
  
      # Compute output by doing t step() on the gru cell
      outputs_t, output_t, h_t, hidden_init = self.gru
      hidden = np.full(
          (self.batch_size, self.hidden_dim), hidden_init.eval()
      )
  
      grads_step_o_i = tf.gradients(output_t, step_input_var)
      grads_step_o_h = tf.gradients(output_t, self.step_hidden_var)
      grads_step_h = tf.gradients(h_t, step_input_var)
  
      self.sess.run(
          [grads_step_o_i, grads_step_o_h, grads_step_h],
          feed_dict={
              step_input_var: obs_input,
              self.step_hidden_var: hidden,
          },
      )  # noqa: E126
  
      grads_step_o_i = tf.gradients(outputs_t, step_input_var)
      grads_step_o_h = tf.gradients(outputs_t, self.step_hidden_var)
      grads_step_h = tf.gradients(h_t, input_var)
  
      # No gradient flow
      with pytest.raises(TypeError):
          self.sess.run(
              grads_step_o_i,
              feed_dict={
                  step_input_var: obs_input,
                  self.step_hidden_var: hidden,
              },
          )
      with pytest.raises(TypeError):
          self.sess.run(
              grads_step_o_h,
              feed_dict={
                  step_input_var: obs_input,
                  self.step_hidden_var: hidden,
              },
          )
      with pytest.raises(TypeError):
          self.sess.run(grads_step_h, feed_dict={input_var: obs_inputs})
  
file_path: garage/tests/garage/tf/models/test_gru.py
imports: import numpy
  import pytest
  import tensorflow
  from garage.tf.models.gru import gru
  from tests.fixtures import TfGraphTestCase
  from tests.helpers import recurrent_step_gru
label_desc: Use image node_id 5 for calling the TestGRU obj's underlying member method code with example usage: obj.test_gradient_paths() without return types