diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 4f71c7398ec..d10dfc3b5f5 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -3580,7 +3580,7 @@ func BoostedTreesSparseCalculateBestFeatureSplitSplitType(value string) BoostedT
 //	l1: l1 regularization factor on leaf weights, per instance based.
 //	l2: l2 regularization factor on leaf weights, per instance based.
 //	tree_complexity: adjustment to the gain, per leaf based.
-//	min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
+//	min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
 //	logits_dimension: The dimension of logit, i.e., number of classes.
 //
 // Returns:
@@ -3677,7 +3677,7 @@ func BoostedTreesCalculateBestFeatureSplitV2(scope *Scope, node_id_range tf.Outp
 //	l1: l1 regularization factor on leaf weights, per instance based.
 //	l2: l2 regularization factor on leaf weights, per instance based.
 //	tree_complexity: adjustment to the gain, per leaf based.
-//	min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
+//	min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
 //	max_splits: the number of nodes that can be split in the whole tree. Used as a dimension of output tensors.
 //
 // Returns:
@@ -3730,7 +3730,7 @@ func BoostedTreesCalculateBestGainsPerFeature(scope *Scope, node_id_range tf.Out
 // Checks whether a tree ensemble has been initialized.
 //
 // Arguments:
-//	tree_ensemble_handle: Handle to the tree ensemble resouce.
+//	tree_ensemble_handle: Handle to the tree ensemble resource.
 //
 // Returns output boolean on whether it is initialized or not.
 func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Output) (is_initialized tf.Output) {
@@ -5126,7 +5126,7 @@ func CudnnRNNParamsToCanonicalV2NumProj(value int64) CudnnRNNParamsToCanonicalV2
 // num_layers: Specifies the number of layers in the RNN model.
 // num_units: Specifies the size of the hidden state.
 // input_size: Specifies the size of the input state.
-// num_params_weigths: number of weight parameter matrix for all layers.
+// num_params_weights: number of weight parameter matrix for all layers.
 // num_params_biases: number of bias parameter vector for all layers.
 // weights: the canonical form of weights that can be used for saving
 //     and restoration. They are more likely to be compatible across different
@@ -8344,7 +8344,7 @@ func BoostedTreesCalculateBestFeatureSplitSplitType(value string) BoostedTreesCa
 //	l1: l1 regularization factor on leaf weights, per instance based.
 //	l2: l2 regularization factor on leaf weights, per instance based.
 //	tree_complexity: adjustment to the gain, per leaf based.
-//	min_node_weight: mininum avg of hessians in a node before required for the node to be considered for splitting.
+//	min_node_weight: minimum avg of hessians in a node before required for the node to be considered for splitting.
 //	logits_dimension: The dimension of logit, i.e., number of classes.
 //
 // Returns:
@@ -11692,7 +11692,7 @@ func DepthwiseConv2dNativeBackpropFilterDataFormat(value string) DepthwiseConv2d
 // element on that dimension. The dimension order is determined by the value of
 // `data_format`, see above for details. Dilations in the batch and depth
 // dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func DepthwiseConv2dNativeBackpropFilterDilations(value []int64) DepthwiseConv2dNativeBackpropFilterAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -11949,7 +11949,7 @@ func SampleDistortedBoundingBoxV2Seed2(value int64) SampleDistortedBoundingBoxV2
 //
 // value: The cropped area of the image must have an aspect ratio =
 // width / height within this range.
-// If not specified, defaults to {f:0.75 f:1.33}
+// If not specified, defaults to {f:0.75  f:1.33}
 func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistortedBoundingBoxV2Attr {
 	return func(m optionalAttr) {
 		m["aspect_ratio_range"] = value
@@ -11960,7 +11960,7 @@ func SampleDistortedBoundingBoxV2AspectRatioRange(value []float32) SampleDistort
 //
 // value: The cropped area of the image must contain a fraction of the
 // supplied image within this range.
-// If not specified, defaults to {f:0.05 f:1}
+// If not specified, defaults to {f:0.05  f:1}
 func SampleDistortedBoundingBoxV2AreaRange(value []float32) SampleDistortedBoundingBoxV2Attr {
 	return func(m optionalAttr) {
 		m["area_range"] = value
@@ -12166,7 +12166,7 @@ func SampleDistortedBoundingBoxMinObjectCovered(value float32) SampleDistortedBo
 //
 // value: The cropped area of the image must have an aspect ratio =
 // width / height within this range.
-// If not specified, defaults to {f:0.75 f:1.33}
+// If not specified, defaults to {f:0.75  f:1.33}
 func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistortedBoundingBoxAttr {
 	return func(m optionalAttr) {
 		m["aspect_ratio_range"] = value
@@ -12177,7 +12177,7 @@ func SampleDistortedBoundingBoxAspectRatioRange(value []float32) SampleDistorted
 //
 // value: The cropped area of the image must contain a fraction of the
 // supplied image within this range.
-// If not specified, defaults to {f:0.05 f:1}
+// If not specified, defaults to {f:0.05  f:1}
 func SampleDistortedBoundingBoxAreaRange(value []float32) SampleDistortedBoundingBoxAttr {
 	return func(m optionalAttr) {
 		m["area_range"] = value
@@ -13731,7 +13731,7 @@ func DebugNumericSummaryV2OutputDtype(value tf.DataType) DebugNumericSummaryV2At
 //   element is a bit which is set to 1 if the input tensor has an
 //   infinity or nan value, or zero otherwise.
 //
-//   3 (CONCISE_HEALTH): Ouput a float32/64 tensor of shape [5]. The 1st
+//   3 (CONCISE_HEALTH): Output a float32/64 tensor of shape [5]. The 1st
 //   element is the tensor_id, if provided, and -1 otherwise. The
 //   remaining four slots are the total number of elements, -infs,
 //   +infs, and nans in the input tensor respectively.
@@ -14089,11 +14089,11 @@ func TridiagonalSolve(scope *Scope, diagonals tf.Output, rhs tf.Output, optional
 //
 // Arguments:
 //	superdiag: Tensor of shape `[..., 1, M]`, representing superdiagonals of
-// tri-diagonal matrices to the left of multiplication. Last element is ingored.
+// tri-diagonal matrices to the left of multiplication. Last element is ignored.
 //	maindiag: Tensor of shape `[..., 1, M]`, representing main diagonals of tri-diagonal
 // matrices to the left of multiplication.
 //	subdiag: Tensor of shape `[..., 1, M]`, representing subdiagonals of tri-diagonal
-// matrices to the left of multiplication. First element is ingored.
+// matrices to the left of multiplication. First element is ignored.
 //	rhs: Tensor of shape `[..., M, N]`, representing MxN matrices to the right of
 // multiplication.
 //
@@ -17710,7 +17710,7 @@ func CudnnRNNCanonicalToParamsV2NumProj(value int64) CudnnRNNCanonicalToParamsV2
 // biases: the canonical form of biases that can be used for saving
 //     and restoration. They are more likely to be compatible across different
 //     generations.
-// num_params_weigths: number of weight parameter matrix for all layers.
+// num_params_weights: number of weight parameter matrix for all layers.
 // num_params_biases: number of bias parameter vector for all layers.
 // rnn_mode: Indicates the type of the RNN model.
 // input_mode: Indicate whether there is a linear projection between the input and
@@ -19018,7 +19018,7 @@ func ImageSummaryMaxImages(value int64) ImageSummaryAttr {
 // ImageSummaryBadColor sets the optional bad_color attribute to value.
 //
 // value: Color to use for pixels with non-finite values.
-// If not specified, defaults to {dtype:DT_UINT8 tensor_shape:{dim:{size:4}} int_val:255 int_val:0 int_val:0 int_val:255}
+// If not specified, defaults to {dtype:DT_UINT8  tensor_shape:{dim:{size:4}}  int_val:255  int_val:0  int_val:0  int_val:255}
 func ImageSummaryBadColor(value tf.Tensor) ImageSummaryAttr {
 	return func(m optionalAttr) {
 		m["bad_color"] = value
@@ -20089,7 +20089,7 @@ func Conv3DBackpropFilterV2DataFormat(value string) Conv3DBackpropFilterV2Attr {
 // filter element on that dimension. The dimension order is determined by the
 // value of `data_format`, see above for details. Dilations in the batch and
 // depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1  i:1}
 func Conv3DBackpropFilterV2Dilations(value []int64) Conv3DBackpropFilterV2Attr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -21357,7 +21357,7 @@ func Conv2DBackpropInputDataFormat(value string) Conv2DBackpropInputAttr {
 // element on that dimension. The dimension order is determined by the value of
 // `data_format`, see above for details. Dilations in the batch and depth
 // dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func Conv2DBackpropInputDilations(value []int64) Conv2DBackpropInputAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -22065,7 +22065,7 @@ func Conv2DDataFormat(value string) Conv2DAttr {
 // filter element on that dimension. The dimension order is determined by the
 // value of `data_format`, see above for details. Dilations in the batch and
 // depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func Conv2DDilations(value []int64) Conv2DAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -22261,7 +22261,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeOutType(value tf.DataTy
 // QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations sets the optional dilations attribute to value.
 //
 // value: List of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAndRequantizeAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -22330,7 +22330,7 @@ func QuantizedDepthwiseConv2DWithBiasAndReluOutType(value tf.DataType) Quantized
 // QuantizedDepthwiseConv2DWithBiasAndReluDilations sets the optional dilations attribute to value.
 //
 // value: List of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func QuantizedDepthwiseConv2DWithBiasAndReluDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAndReluAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -22445,7 +22445,7 @@ func QuantizedDepthwiseConv2DWithBiasOutType(value tf.DataType) QuantizedDepthwi
 // QuantizedDepthwiseConv2DWithBiasDilations sets the optional dilations attribute to value.
 //
 // value: List of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func QuantizedDepthwiseConv2DWithBiasDilations(value []int64) QuantizedDepthwiseConv2DWithBiasAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -22504,7 +22504,7 @@ func QuantizedDepthwiseConv2DOutType(value tf.DataType) QuantizedDepthwiseConv2D
 // QuantizedDepthwiseConv2DDilations sets the optional dilations attribute to value.
 //
 // value: List of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func QuantizedDepthwiseConv2DDilations(value []int64) QuantizedDepthwiseConv2DAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -22678,7 +22678,7 @@ func QuantizedConv2DPerChannelOutType(value tf.DataType) QuantizedConv2DPerChann
 // QuantizedConv2DPerChannelDilations sets the optional dilations attribute to value.
 //
 // value: list of dilation values.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func QuantizedConv2DPerChannelDilations(value []int64) QuantizedConv2DPerChannelAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -23059,7 +23059,7 @@ func Conv3DBackpropInputV2DataFormat(value string) Conv3DBackpropInputV2Attr {
 // filter element on that dimension. The dimension order is determined by the
 // value of `data_format`, see above for details. Dilations in the batch and
 // depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1  i:1}
 func Conv3DBackpropInputV2Dilations(value []int64) Conv3DBackpropInputV2Attr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -25499,7 +25499,7 @@ func AvgPool3DGrad(scope *Scope, orig_input_shape tf.Output, grad tf.Output, ksi
 type Conv3DBackpropFilterAttr func(optionalAttr)
 
 // Conv3DBackpropFilterDilations sets the optional dilations attribute to value.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1  i:1}
 func Conv3DBackpropFilterDilations(value []int64) Conv3DBackpropFilterAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -25831,7 +25831,7 @@ func DepthwiseConv2dNativeBackpropInputDataFormat(value string) DepthwiseConv2dN
 // element on that dimension. The dimension order is determined by the value of
 // `data_format`, see above for details. Dilations in the batch and depth
 // dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func DepthwiseConv2dNativeBackpropInputDilations(value []int64) DepthwiseConv2dNativeBackpropInputAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -25881,7 +25881,7 @@ func DepthwiseConv2dNativeBackpropInput(scope *Scope, input_sizes tf.Output, fil
 type Conv3DBackpropInputAttr func(optionalAttr)
 
 // Conv3DBackpropInputDilations sets the optional dilations attribute to value.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1  i:1}
 func Conv3DBackpropInputDilations(value []int64) Conv3DBackpropInputAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -26131,7 +26131,7 @@ func DepthwiseConv2dNativeDataFormat(value string) DepthwiseConv2dNativeAttr {
 // element on that dimension. The dimension order is determined by the value of
 // `data_format`, see above for details. Dilations in the batch and depth
 // dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func DepthwiseConv2dNativeDilations(value []int64) DepthwiseConv2dNativeAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -26761,7 +26761,7 @@ func QuantizedConv2DOutType(value tf.DataType) QuantizedConv2DAttr {
 // filter element on that dimension. The dimension order is determined by the
 // value of `data_format`, see above for details. Dilations in the batch and
 // depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func QuantizedConv2DDilations(value []int64) QuantizedConv2DAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -27826,7 +27826,7 @@ func Conv3DDataFormat(value string) Conv3DAttr {
 // filter element on that dimension. The dimension order is determined by the
 // value of `data_format`, see above for details. Dilations in the batch and
 // depth dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1  i:1}
 func Conv3DDilations(value []int64) Conv3DAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value
@@ -31238,8 +31238,8 @@ func ResourceApplyFtrlV2UseLocking(value bool) ResourceApplyFtrlV2Attr {
 //	linear: Should be from a Variable().
 //	grad: The gradient.
 //	lr: Scaling factor. Must be a scalar.
-//	l1: L1 regulariation. Must be a scalar.
-//	l2: L2 shrinkage regulariation. Must be a scalar.
+//	l1: L1 regularization. Must be a scalar.
+//	l2: L2 shrinkage regularization. Must be a scalar.
 //
 //	lr_power: Scaling factor. Must be a scalar.
 //
@@ -36706,8 +36706,8 @@ func ResourceApplyFtrlUseLocking(value bool) ResourceApplyFtrlAttr {
 //	linear: Should be from a Variable().
 //	grad: The gradient.
 //	lr: Scaling factor. Must be a scalar.
-//	l1: L1 regulariation. Must be a scalar.
-//	l2: L2 regulariation. Must be a scalar.
+//	l1: L1 regularization. Must be a scalar.
+//	l2: L2 regularization. Must be a scalar.
 //	lr_power: Scaling factor. Must be a scalar.
 //
 // Returns the created operation.
@@ -43228,7 +43228,7 @@ func ResourceSparseApplyFtrlV2UseLocking(value bool) ResourceSparseApplyFtrlV2At
 //	indices: A vector of indices into the first dimension of var and accum.
 //	lr: Scaling factor. Must be a scalar.
 //	l1: L1 regularization. Must be a scalar.
-//	l2: L2 shrinkage regulariation. Must be a scalar.
+//	l2: L2 shrinkage regularization. Must be a scalar.
 //
 //	lr_power: Scaling factor. Must be a scalar.
 //
@@ -45536,7 +45536,7 @@ func Conv2DBackpropFilterDataFormat(value string) Conv2DBackpropFilterAttr {
 // element on that dimension. The dimension order is determined by the value of
 // `data_format`, see above for details. Dilations in the batch and depth
 // dimensions must be 1.
-// If not specified, defaults to {i:1 i:1 i:1 i:1}
+// If not specified, defaults to {i:1  i:1  i:1  i:1}
 func Conv2DBackpropFilterDilations(value []int64) Conv2DBackpropFilterAttr {
 	return func(m optionalAttr) {
 		m["dilations"] = value