Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 241410512

tensorflow/go/op/wrappers.go

@@ -7970,6 +7970,43 @@ func DynamicStitch(scope *Scope, indices []tf.Output, data []tf.Output) (merged
 // dimension be equal to sizeof(`type`)/sizeof(`T`). The shape then goes from
 // [..., sizeof(`type`)/sizeof(`T`)] to [...].
 //
+// 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:
+// ```python
+// >>> a = [1., 2., 3.]
+// >>> equality_bitcast = tf.bitcast(a,tf.complex128)
+// tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot bitcast from float to complex128: shape [3] [Op:Bitcast]
+// >>> equality_cast = tf.cast(a,tf.complex128)
+// >>> print(equality_cast)
+// tf.Tensor([1.+0.j 2.+0.j 3.+0.j], shape=(3,), dtype=complex128)
+// ```
+// Example 2:
+// ```python
+// >>> tf.bitcast(tf.constant(0xffffffff, dtype=tf.uint32), tf.uint8)
+// <tf.Tensor: ... shape=(4,), dtype=uint8, numpy=array([255, 255, 255, 255], dtype=uint8)>
+// ```
+// Example 3:
+// ```python
+// >>> x = [1., 2., 3.]
+// >>> y = [0., 2., 3.]
+// >>> equality= tf.equal(x,y)
+// >>> equality_cast = tf.cast(equality,tf.float32)
+// >>> equality_bitcast = tf.bitcast(equality_cast,tf.uint8)
+// >>> print(equality)
+// tf.Tensor([False True True], shape=(3,), dtype=bool)
+// >>> print(equality_cast)
+// tf.Tensor([0. 1. 1.], shape=(3,), dtype=float32)
+// >>> print(equality_bitcast)
+// tf.Tensor(
+// [[ 0 0 0 0]
+//  [ 0 0 128 63]
+//  [ 0 0 128 63]], shape=(3, 4), dtype=uint8)
+// ```
+//
 // *NOTE*: Bitcast is implemented as a low-level cast, so machines with different
 // endian orderings will give different results.
 func Bitcast(scope *Scope, input tf.Output, type_ tf.DataType) (output tf.Output) {