tf.bitcast() and tf.cast() work differently when real dtype is casted as a complex dtype
(e.g. tf.complex64 or tf.complex128) as tf.cast() make imaginary part 0 while tf.bitcast()
gives module error.
For example,

Example 1:

>>> a = [1., 2., 3.]
>>> equality_bitcast = tf.bitcast(a, tf.complex128)
Traceback (most recent call last):
...

				core.ResourceName(core.ResourceStorage): resource.MustParse("10G"),
			},
		},
	})

	validClaimStorageClassInAnnotationAndSpec := testVolumeClaimStorageClassInAnnotationAndSpec(
		"foo", "ns", "fast", "fast", core.PersistentVolumeClaimSpec{
			AccessModes: []core.PersistentVolumeAccessMode{
				core.ReadOnlyMany,
			},
			Resources: core.VolumeResourceRequirements{
				Requests: core.ResourceList{

*   `tf.lite`:

    *   Add experimental API `experimental_from_jax` to support conversion from
        Jax models to TensorFlow Lite.
    *   Support uint32 data type for cast op.
    *   Support int8 data type for cast op.
    *   Add experimental quantization debugger `tf.lite.QuantizationDebugger`
    *   Add lite.experimental.authoring.compatible API

              }
            ],
            "description": "Last time the condition transitioned from one status to another."
          },
          "message": {
            "description": "A human readable message indicating details about the transition.",
            "type": "string"
          },
          "reason": {
            "description": "The reason for the condition's last transition.",
            "type": "string"
          },