class_docstr: Tests the correctness of the Y = JointMap({nested}) transformation.
code: def testMixedDtypeLogDetJacobian(self):
      bij = joint_map.JointMap(
          {
              "a": scale.Scale(tf.constant(1, dtype=tf.float16)),
              "b": scale.Scale(tf.constant(2, dtype=tf.float32)),
              "c": scale.Scale(tf.constant(3, dtype=tf.float64)),
          }
      )
  
      fldj = bij.forward_log_det_jacobian(
          x={"a": 4, "b": 5, "c": 6},
          event_ndims=dict.fromkeys("abc", 0),
      )
      self.assertDTypeEqual(fldj, np.float64)
      self.assertAllClose(
          np.log(1) + np.log(2) + np.log(3), self.evaluate(fldj)
      )
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import exp
  from tensorflow_probability.python.bijectors import invert
  from tensorflow_probability.python.bijectors import joint_map
  from tensorflow_probability.python.bijectors import ldj_ratio
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import scale
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 5 for calling the JointMapBijectorTest obj's underlying member method code with example usage: obj.testMixedDtypeLogDetJacobian() without return types