Go: Update generated wrapper functions for TensorFlow ops.

Change: 149040924

tensorflow/go/op/wrappers.go

@@ -10134,9 +10134,9 @@ func Conv2DUseCudnnOnGpu(value bool) Conv2DAttr {
 //
 // value: Specify the data format of the input and output data. With the
 // default format "NHWC", the data is stored in the order of:
-//     [batch, in_height, in_width, in_channels].
+//     [batch, height, width, channels].
 // Alternatively, the format could be "NCHW", the data storage order of:
-//     [batch, in_channels, in_height, in_width].
+//     [batch, channels, height, width].
 // If not specified, defaults to s:"NHWC"
 func Conv2DDataFormat(value string) Conv2DAttr {
 	return func(m optionalAttr) {
@@ -10169,11 +10169,17 @@ func Conv2DDataFormat(value string) Conv2DAttr {
 // horizontal and vertices strides, `strides = [1, stride, stride, 1]`.
 //
 // Arguments:
-//
+//	input: A 4-D tensor. The dimension order is interpreted according to the value
-//
+// of `data_format`, see below for details.
-//	strides: 1-D of length 4.  The stride of the sliding window for each dimension
+//	filter: A 4-D tensor of shape
-// of `input`. Must be in the same order as the dimension specified with format.
+// `[filter_height, filter_width, in_channels, out_channels]`
+//	strides: 1-D tensor of length 4.  The stride of the sliding window for each
+// dimension of `input`. The dimension order is determined by the value of
+//   `data_format`, see below for details.
 //	padding: The type of padding algorithm to use.
+//
+// Returns A 4-D tensor. The dimension order is determined by the value of
+// `data_format`, see below for details.
 func Conv2D(scope *Scope, input tf.Output, filter tf.Output, strides []int64, padding string, optional ...Conv2DAttr) (output tf.Output) {
 	if scope.Err() != nil {
 		return