docstr: Test if the bijector identifies dtype correctly.
docstr_tok: ["Test","if","the","bijector","identifies","dtype","correctly","."]
code: def testDtypes(self):
      """Test if the bijector identifies dtype correctly."""
      ims = self._make_images()
      bijection = self._create_glow(False)
      z = bijection.inverse(ims)
  
      def float64_net(input_shape):
          input_nchan = input_shape[-1]
          return tf_keras.Sequential(
              [
                  tf_keras.layers.Input(input_shape, dtype=tf.float64),
                  tf_keras.layers.Conv2D(
                      2 * input_nchan,
                      3,
                      padding="same",
                      dtype=tf.float64,
                  ),
              ]
          )
  
      def float64_exit(input_shape, output_chan):
          return tf_keras.Sequential(
              [
                  tf_keras.layers.Input(input_shape, dtype=tf.float64),
                  tf_keras.layers.Conv2D(
                      2 * output_chan,
                      3,
                      padding="same",
                      dtype=tf.float64,
                  ),
              ]
          )
  
      float64_bijection = glow.Glow(
          output_shape=self.output_shape,
          num_glow_blocks=2,
          num_steps_per_block=1,
          coupling_bijector_fn=float64_net,
          exit_bijector_fn=float64_exit,
          grab_after_block=[0.5, 0.5],
      )
      zf64 = float64_bijection.inverse(tf.cast(ims, tf.float64))
      self.evaluate([v.initializer for v in bijection.variables])
      self.evaluate(
          [v.initializer for v in float64_bijection.variables]
      )
      self.assertAllFinite(self.evaluate(z))
      self.assertAllFinite(self.evaluate(zf64))
  
imports: import functools
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import blockwise
  from tensorflow_probability.python.bijectors import chain
  from tensorflow_probability.python.bijectors import composition
  from tensorflow_probability.python.bijectors import glow
  from tensorflow_probability.python.bijectors import identity
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.math.gradient import batch_jacobian
label_desc: Use image node_id 13 for calling the GlowTest obj's underlying member method code with example usage: obj.testDtypes() and returns: tf_keras, tf_keras