Go: Update generated wrapper functions for TensorFlow ops.

PiperOrigin-RevId: 168917534

1 files changed, 422 insertions, 422 deletions

diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go
index 97956801d5..7dbe115dd6 100644
--- a/tensorflow/go/op/wrappers.go
+++ b/tensorflow/go/op/wrappers.go
@@ -119,27 +119,6 @@ func VariableShape(scope *Scope, input tf.Output, optional ...VariableShapeAttr)
 	return op.Output(0)
 }
 
-// Checks whether a resource handle-based variable has been initialized.
-//
-// Arguments:
-//	resource: the input resource handle.
-//
-// Returns a scalar boolean which is true if the variable has been
-// initialized.
-func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "VarIsInitializedOp",
-		Input: []tf.Input{
-			resource,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Assigns a new value to a variable.
 //
 // Any ReadVariableOp with a control dependency on this op is guaranteed to return
@@ -7757,6 +7736,100 @@ func ShardedFilename(scope *Scope, basename tf.Output, shard tf.Output, num_shar
 	return op.Output(0)
 }
 
+// Saves input tensors slices to disk.
+//
+// This is like `Save` except that tensors can be listed in the saved file as being
+// a slice of a larger tensor.  `shapes_and_slices` specifies the shape of the
+// larger tensor and the slice that this tensor covers. `shapes_and_slices` must
+// have as many elements as `tensor_names`.
+//
+// Elements of the `shapes_and_slices` input must either be:
+//
+// *  The empty string, in which case the corresponding tensor is
+//    saved normally.
+// *  A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the
+//    `dimI` are the dimensions of the larger tensor and `slice-spec`
+//    specifies what part is covered by the tensor to save.
+//
+// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1`
+// where each `sliceI` is either:
+//
+// *  The string `-` meaning that the slice covers all indices of this dimension
+// *  `start,length` where `start` and `length` are integers.  In that
+//    case the slice covers `length` indices starting at `start`.
+//
+// See also `Save`.
+//
+// Arguments:
+//	filename: Must have a single element. The name of the file to which we write the
+// tensor.
+//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
+//	shapes_and_slices: Shape `[N]`.  The shapes and slice specifications to use when
+// saving the tensors.
+//	data: `N` tensors to save.
+//
+// Returns the created operation.
+func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "SaveSlices",
+		Input: []tf.Input{
+			filename, tensor_names, shapes_and_slices, tf.OutputList(data),
+		},
+	}
+	return scope.AddOperation(opspec)
+}
+
+// MergeV2CheckpointsAttr is an optional argument to MergeV2Checkpoints.
+type MergeV2CheckpointsAttr func(optionalAttr)
+
+// MergeV2CheckpointsDeleteOldDirs sets the optional delete_old_dirs attribute to value.
+//
+// value: see above.
+// If not specified, defaults to true
+func MergeV2CheckpointsDeleteOldDirs(value bool) MergeV2CheckpointsAttr {
+	return func(m optionalAttr) {
+		m["delete_old_dirs"] = value
+	}
+}
+
+// V2 format specific: merges the metadata files of sharded checkpoints.  The
+//
+// result is one logical checkpoint, with one physical metadata file and renamed
+// data files.
+//
+// Intended for "grouping" multiple checkpoints in a sharded checkpoint setup.
+//
+// If delete_old_dirs is true, attempts to delete recursively the dirname of each
+// path in the input checkpoint_prefixes.  This is useful when those paths are non
+// user-facing temporary locations.
+//
+// Arguments:
+//	checkpoint_prefixes: prefixes of V2 checkpoints to merge.
+//	destination_prefix: scalar.  The desired final prefix.  Allowed to be the same
+// as one of the checkpoint_prefixes.
+//
+// Returns the created operation.
+func MergeV2Checkpoints(scope *Scope, checkpoint_prefixes tf.Output, destination_prefix tf.Output, optional ...MergeV2CheckpointsAttr) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "MergeV2Checkpoints",
+		Input: []tf.Input{
+			checkpoint_prefixes, destination_prefix,
+		},
+		Attrs: attrs,
+	}
+	return scope.AddOperation(opspec)
+}
+
 // AudioSpectrogramAttr is an optional argument to AudioSpectrogram.
 type AudioSpectrogramAttr func(optionalAttr)
 
@@ -9378,52 +9451,6 @@ func SegmentMax(scope *Scope, data tf.Output, segment_ids tf.Output) (output tf.
 	return op.Output(0)
 }
 
-// Saves input tensors slices to disk.
-//
-// This is like `Save` except that tensors can be listed in the saved file as being
-// a slice of a larger tensor.  `shapes_and_slices` specifies the shape of the
-// larger tensor and the slice that this tensor covers. `shapes_and_slices` must
-// have as many elements as `tensor_names`.
-//
-// Elements of the `shapes_and_slices` input must either be:
-//
-// *  The empty string, in which case the corresponding tensor is
-//    saved normally.
-// *  A string of the form `dim0 dim1 ... dimN-1 slice-spec` where the
-//    `dimI` are the dimensions of the larger tensor and `slice-spec`
-//    specifies what part is covered by the tensor to save.
-//
-// `slice-spec` itself is a `:`-separated list: `slice0:slice1:...:sliceN-1`
-// where each `sliceI` is either:
-//
-// *  The string `-` meaning that the slice covers all indices of this dimension
-// *  `start,length` where `start` and `length` are integers.  In that
-//    case the slice covers `length` indices starting at `start`.
-//
-// See also `Save`.
-//
-// Arguments:
-//	filename: Must have a single element. The name of the file to which we write the
-// tensor.
-//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
-//	shapes_and_slices: Shape `[N]`.  The shapes and slice specifications to use when
-// saving the tensors.
-//	data: `N` tensors to save.
-//
-// Returns the created operation.
-func SaveSlices(scope *Scope, filename tf.Output, tensor_names tf.Output, shapes_and_slices tf.Output, data []tf.Output) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "SaveSlices",
-		Input: []tf.Input{
-			filename, tensor_names, shapes_and_slices, tf.OutputList(data),
-		},
-	}
-	return scope.AddOperation(opspec)
-}
-
 // Returns the rank of a tensor.
 //
 // This operation returns an integer representing the rank of `input`.
@@ -13132,33 +13159,6 @@ func DepthwiseConv2dNativeBackpropFilter(scope *Scope, input tf.Output, filter_s
 	return op.Output(0)
 }
 
-// Saves the input tensors to disk.
-//
-// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
-// is written to `filename` with name `tensor_names[i]`.
-//
-// See also `SaveSlices`.
-//
-// Arguments:
-//	filename: Must have a single element. The name of the file to which we write
-// the tensor.
-//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
-//	data: `N` tensors to save.
-//
-// Returns the created operation.
-func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Save",
-		Input: []tf.Input{
-			filename, tensor_names, tf.OutputList(data),
-		},
-	}
-	return scope.AddOperation(opspec)
-}
-
 // Shuffle dimensions of x according to a permutation.
 //
 // The output `y` has the same rank as `x`. The shapes of `x` and `y` satisfy:
@@ -15248,6 +15248,80 @@ func Tanh(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
+// Restores tensors from a V2 checkpoint.
+//
+// For backward compatibility with the V1 format, this Op currently allows
+// restoring from a V1 checkpoint as well:
+//   - This Op first attempts to find the V2 index file pointed to by "prefix", and
+//     if found proceed to read it as a V2 checkpoint;
+//   - Otherwise the V1 read path is invoked.
+// Relying on this behavior is not recommended, as the ability to fall back to read
+// V1 might be deprecated and eventually removed.
+//
+// By default, restores the named tensors in full.  If the caller wishes to restore
+// specific slices of stored tensors, "shape_and_slices" should be non-empty
+// strings and correspondingly well-formed.
+//
+// Callers must ensure all the named tensors are indeed stored in the checkpoint.
+//
+// Arguments:
+//	prefix: Must have a single element.  The prefix of a V2 checkpoint.
+//	tensor_names: shape {N}.  The names of the tensors to be restored.
+//	shape_and_slices: shape {N}.  The slice specs of the tensors to be restored.
+// Empty strings indicate that they are non-partitioned tensors.
+//	dtypes: shape {N}.  The list of expected dtype for the tensors.  Must match
+// those stored in the checkpoint.
+//
+// Returns shape {N}.  The restored tensors, whose shapes are read from the
+// checkpoint directly.
+func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"dtypes": dtypes}
+	opspec := tf.OpSpec{
+		Type: "RestoreV2",
+		Input: []tf.Input{
+			prefix, tensor_names, shape_and_slices,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	if scope.Err() != nil {
+		return
+	}
+	var idx int
+	var err error
+	if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil {
+		scope.UpdateErr("RestoreV2", err)
+		return
+	}
+	return tensors
+}
+
+// Returns x / y element-wise for integer types.
+//
+// Truncation designates that negative numbers will round fractional quantities
+// toward zero. I.e. -7 / 5 = 1. This matches C semantics but it is different
+// than Python semantics. See `FloorDiv` for a division function that matches
+// Python Semantics.
+//
+// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting
+// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
+func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "TruncateDiv",
+		Input: []tf.Input{
+			x, y,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // MaxPoolGradGradV2Attr is an optional argument to MaxPoolGradGradV2.
 type MaxPoolGradGradV2Attr func(optionalAttr)
 
@@ -16448,6 +16522,27 @@ func LoadAndRemapMatrix(scope *Scope, ckpt_path tf.Output, old_tensor_name tf.Ou
 	return op.Output(0)
 }
 
+// Checks whether a resource handle-based variable has been initialized.
+//
+// Arguments:
+//	resource: the input resource handle.
+//
+// Returns a scalar boolean which is true if the variable has been
+// initialized.
+func VarIsInitializedOp(scope *Scope, resource tf.Output) (is_initialized tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "VarIsInitializedOp",
+		Input: []tf.Input{
+			resource,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // ResizeAreaAttr is an optional argument to ResizeArea.
 type ResizeAreaAttr func(optionalAttr)
 
@@ -18087,7 +18182,7 @@ func ReaderSerializeStateV2(scope *Scope, reader_handle tf.Output) (state tf.Out
 // position = [1, 5, 7]
 // length =   [3, 2, 1]
 //
-// output = [b'hir', b'ee', b'n"]
+// output = [b'hir', b'ee', b'n']
 // ```
 //
 // Arguments:
@@ -18762,54 +18857,6 @@ func QuantizedReluX(scope *Scope, features tf.Output, max_value tf.Output, min_f
 	return op.Output(0), op.Output(1), op.Output(2)
 }
 
-// MergeV2CheckpointsAttr is an optional argument to MergeV2Checkpoints.
-type MergeV2CheckpointsAttr func(optionalAttr)
-
-// MergeV2CheckpointsDeleteOldDirs sets the optional delete_old_dirs attribute to value.
-//
-// value: see above.
-// If not specified, defaults to true
-func MergeV2CheckpointsDeleteOldDirs(value bool) MergeV2CheckpointsAttr {
-	return func(m optionalAttr) {
-		m["delete_old_dirs"] = value
-	}
-}
-
-// V2 format specific: merges the metadata files of sharded checkpoints.  The
-//
-// result is one logical checkpoint, with one physical metadata file and renamed
-// data files.
-//
-// Intended for "grouping" multiple checkpoints in a sharded checkpoint setup.
-//
-// If delete_old_dirs is true, attempts to delete recursively the dirname of each
-// path in the input checkpoint_prefixes.  This is useful when those paths are non
-// user-facing temporary locations.
-//
-// Arguments:
-//	checkpoint_prefixes: prefixes of V2 checkpoints to merge.
-//	destination_prefix: scalar.  The desired final prefix.  Allowed to be the same
-// as one of the checkpoint_prefixes.
-//
-// Returns the created operation.
-func MergeV2Checkpoints(scope *Scope, checkpoint_prefixes tf.Output, destination_prefix tf.Output, optional ...MergeV2CheckpointsAttr) (o *tf.Operation) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "MergeV2Checkpoints",
-		Input: []tf.Input{
-			checkpoint_prefixes, destination_prefix,
-		},
-		Attrs: attrs,
-	}
-	return scope.AddOperation(opspec)
-}
-
 // UnpackAttr is an optional argument to Unpack.
 type UnpackAttr func(optionalAttr)
 
@@ -22188,101 +22235,6 @@ func Rsqrt(scope *Scope, x tf.Output) (y tf.Output) {
 	return op.Output(0)
 }
 
-// RecordInputAttr is an optional argument to RecordInput.
-type RecordInputAttr func(optionalAttr)
-
-// RecordInputFileRandomSeed sets the optional file_random_seed attribute to value.
-//
-// value: Random seeds used to produce randomized records.
-// If not specified, defaults to 301
-func RecordInputFileRandomSeed(value int64) RecordInputAttr {
-	return func(m optionalAttr) {
-		m["file_random_seed"] = value
-	}
-}
-
-// RecordInputFileShuffleShiftRatio sets the optional file_shuffle_shift_ratio attribute to value.
-//
-// value: Shifts the list of files after the list is randomly
-// shuffled.
-// If not specified, defaults to 0
-func RecordInputFileShuffleShiftRatio(value float32) RecordInputAttr {
-	return func(m optionalAttr) {
-		m["file_shuffle_shift_ratio"] = value
-	}
-}
-
-// RecordInputFileBufferSize sets the optional file_buffer_size attribute to value.
-//
-// value: The randomization shuffling buffer.
-// If not specified, defaults to 10000
-func RecordInputFileBufferSize(value int64) RecordInputAttr {
-	return func(m optionalAttr) {
-		m["file_buffer_size"] = value
-	}
-}
-
-// RecordInputFileParallelism sets the optional file_parallelism attribute to value.
-//
-// value: How many sstables are opened and concurrently iterated over.
-// If not specified, defaults to 16
-func RecordInputFileParallelism(value int64) RecordInputAttr {
-	return func(m optionalAttr) {
-		m["file_parallelism"] = value
-	}
-}
-
-// RecordInputBatchSize sets the optional batch_size attribute to value.
-//
-// value: The batch size.
-// If not specified, defaults to 32
-func RecordInputBatchSize(value int64) RecordInputAttr {
-	return func(m optionalAttr) {
-		m["batch_size"] = value
-	}
-}
-
-// Emits randomized records.
-//
-// Arguments:
-//	file_pattern: Glob pattern for the data files.
-//
-// Returns A tensor of shape [batch_size].
-func RecordInput(scope *Scope, file_pattern string, optional ...RecordInputAttr) (records tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"file_pattern": file_pattern}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "RecordInput",
-
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Rounds the values of a tensor to the nearest integer, element-wise.
-//
-// Rounds half to even.  Also known as bankers rounding. If you want to round
-// according to the current system rounding mode use std::cint.
-func Round(scope *Scope, x tf.Output) (y tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "Round",
-		Input: []tf.Input{
-			x,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Generates values in an interval.
 //
 // A sequence of `num` evenly-spaced values are generated beginning at `start`.
@@ -23637,6 +23589,143 @@ func Add(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
 	return op.Output(0)
 }
 
+// Saves the input tensors to disk.
+//
+// The size of `tensor_names` must match the number of tensors in `data`. `data[i]`
+// is written to `filename` with name `tensor_names[i]`.
+//
+// See also `SaveSlices`.
+//
+// Arguments:
+//	filename: Must have a single element. The name of the file to which we write
+// the tensor.
+//	tensor_names: Shape `[N]`. The names of the tensors to be saved.
+//	data: `N` tensors to save.
+//
+// Returns the created operation.
+func Save(scope *Scope, filename tf.Output, tensor_names tf.Output, data []tf.Output) (o *tf.Operation) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Save",
+		Input: []tf.Input{
+			filename, tensor_names, tf.OutputList(data),
+		},
+	}
+	return scope.AddOperation(opspec)
+}
+
+// QrAttr is an optional argument to Qr.
+type QrAttr func(optionalAttr)
+
+// QrFullMatrices sets the optional full_matrices attribute to value.
+//
+// value: If true, compute full-sized `q` and `r`. If false
+// (the default), compute only the leading `P` columns of `q`.
+// If not specified, defaults to false
+func QrFullMatrices(value bool) QrAttr {
+	return func(m optionalAttr) {
+		m["full_matrices"] = value
+	}
+}
+
+// Computes the QR decompositions of one or more matrices.
+//
+// Computes the QR decomposition of each inner matrix in `tensor` such that
+// `tensor[..., :, :] = q[..., :, :] * r[..., :,:])`
+//
+// ```python
+// # a is a tensor.
+// # q is a tensor of orthonormal matrices.
+// # r is a tensor of upper triangular matrices.
+// q, r = qr(a)
+// q_full, r_full = qr(a, full_matrices=True)
+// ```
+//
+// Arguments:
+//	input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
+// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
+//
+// Returns Orthonormal basis for range of `a`. If `full_matrices` is `False` then
+// shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
+// `[..., M, M]`.Triangular factor. If `full_matrices` is `False` then shape is
+// `[..., P, N]`. If `full_matrices` is `True` then shape is `[..., M, N]`.
+func Qr(scope *Scope, input tf.Output, optional ...QrAttr) (q tf.Output, r tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "Qr",
+		Input: []tf.Input{
+			input,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0), op.Output(1)
+}
+
+// AudioSummaryAttr is an optional argument to AudioSummary.
+type AudioSummaryAttr func(optionalAttr)
+
+// AudioSummaryMaxOutputs sets the optional max_outputs attribute to value.
+//
+// value: Max number of batch elements to generate audio for.
+// If not specified, defaults to 3
+//
+// REQUIRES: value >= 1
+func AudioSummaryMaxOutputs(value int64) AudioSummaryAttr {
+	return func(m optionalAttr) {
+		m["max_outputs"] = value
+	}
+}
+
+// Outputs a `Summary` protocol buffer with audio.
+//
+// DEPRECATED at GraphDef version 15: Use AudioSummaryV2.
+//
+// The summary has up to `max_outputs` summary values containing audio. The
+// audio is built from `tensor` which must be 3-D with shape `[batch_size,
+// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
+// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
+//
+// The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
+// build the `tag` of the summary values:
+//
+// *  If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
+// *  If `max_outputs` is greater than 1, the summary value tags are
+//    generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
+//
+// Arguments:
+//	tag: Scalar. Used to build the `tag` attribute of the summary values.
+//	tensor: 2-D of shape `[batch_size, frames]`.
+//	sample_rate: The sample rate of the signal in hertz.
+//
+// Returns Scalar. Serialized `Summary` protocol buffer.
+func AudioSummary(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate float32, optional ...AudioSummaryAttr) (summary tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"sample_rate": sample_rate}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "AudioSummary",
+		Input: []tf.Input{
+			tag, tensor,
+		},
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // BiasAddAttr is an optional argument to BiasAdd.
 type BiasAddAttr func(optionalAttr)
 
@@ -23805,6 +23894,101 @@ func ApproximateEqual(scope *Scope, x tf.Output, y tf.Output, optional ...Approx
 	return op.Output(0)
 }
 
+// RecordInputAttr is an optional argument to RecordInput.
+type RecordInputAttr func(optionalAttr)
+
+// RecordInputFileRandomSeed sets the optional file_random_seed attribute to value.
+//
+// value: Random seeds used to produce randomized records.
+// If not specified, defaults to 301
+func RecordInputFileRandomSeed(value int64) RecordInputAttr {
+	return func(m optionalAttr) {
+		m["file_random_seed"] = value
+	}
+}
+
+// RecordInputFileShuffleShiftRatio sets the optional file_shuffle_shift_ratio attribute to value.
+//
+// value: Shifts the list of files after the list is randomly
+// shuffled.
+// If not specified, defaults to 0
+func RecordInputFileShuffleShiftRatio(value float32) RecordInputAttr {
+	return func(m optionalAttr) {
+		m["file_shuffle_shift_ratio"] = value
+	}
+}
+
+// RecordInputFileBufferSize sets the optional file_buffer_size attribute to value.
+//
+// value: The randomization shuffling buffer.
+// If not specified, defaults to 10000
+func RecordInputFileBufferSize(value int64) RecordInputAttr {
+	return func(m optionalAttr) {
+		m["file_buffer_size"] = value
+	}
+}
+
+// RecordInputFileParallelism sets the optional file_parallelism attribute to value.
+//
+// value: How many sstables are opened and concurrently iterated over.
+// If not specified, defaults to 16
+func RecordInputFileParallelism(value int64) RecordInputAttr {
+	return func(m optionalAttr) {
+		m["file_parallelism"] = value
+	}
+}
+
+// RecordInputBatchSize sets the optional batch_size attribute to value.
+//
+// value: The batch size.
+// If not specified, defaults to 32
+func RecordInputBatchSize(value int64) RecordInputAttr {
+	return func(m optionalAttr) {
+		m["batch_size"] = value
+	}
+}
+
+// Emits randomized records.
+//
+// Arguments:
+//	file_pattern: Glob pattern for the data files.
+//
+// Returns A tensor of shape [batch_size].
+func RecordInput(scope *Scope, file_pattern string, optional ...RecordInputAttr) (records tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	attrs := map[string]interface{}{"file_pattern": file_pattern}
+	for _, a := range optional {
+		a(attrs)
+	}
+	opspec := tf.OpSpec{
+		Type: "RecordInput",
+
+		Attrs: attrs,
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
+// Rounds the values of a tensor to the nearest integer, element-wise.
+//
+// Rounds half to even.  Also known as bankers rounding. If you want to round
+// according to the current system rounding mode use std::cint.
+func Round(scope *Scope, x tf.Output) (y tf.Output) {
+	if scope.Err() != nil {
+		return
+	}
+	opspec := tf.OpSpec{
+		Type: "Round",
+		Input: []tf.Input{
+			x,
+		},
+	}
+	op := scope.AddOperation(opspec)
+	return op.Output(0)
+}
+
 // Returns the max of x and y (i.e. x > y ? x : y) element-wise.
 //
 // *NOTE*: `Maximum` supports broadcasting. More about broadcasting
@@ -25476,116 +25660,6 @@ func AudioSummaryV2(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate t
 	return op.Output(0)
 }
 
-// QrAttr is an optional argument to Qr.
-type QrAttr func(optionalAttr)
-
-// QrFullMatrices sets the optional full_matrices attribute to value.
-//
-// value: If true, compute full-sized `q` and `r`. If false
-// (the default), compute only the leading `P` columns of `q`.
-// If not specified, defaults to false
-func QrFullMatrices(value bool) QrAttr {
-	return func(m optionalAttr) {
-		m["full_matrices"] = value
-	}
-}
-
-// Computes the QR decompositions of one or more matrices.
-//
-// Computes the QR decomposition of each inner matrix in `tensor` such that
-// `tensor[..., :, :] = q[..., :, :] * r[..., :,:])`
-//
-// ```python
-// # a is a tensor.
-// # q is a tensor of orthonormal matrices.
-// # r is a tensor of upper triangular matrices.
-// q, r = qr(a)
-// q_full, r_full = qr(a, full_matrices=True)
-// ```
-//
-// Arguments:
-//	input: A tensor of shape `[..., M, N]` whose inner-most 2 dimensions
-// form matrices of size `[M, N]`. Let `P` be the minimum of `M` and `N`.
-//
-// Returns Orthonormal basis for range of `a`. If `full_matrices` is `False` then
-// shape is `[..., M, P]`; if `full_matrices` is `True` then shape is
-// `[..., M, M]`.Triangular factor. If `full_matrices` is `False` then shape is
-// `[..., P, N]`. If `full_matrices` is `True` then shape is `[..., M, N]`.
-func Qr(scope *Scope, input tf.Output, optional ...QrAttr) (q tf.Output, r tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "Qr",
-		Input: []tf.Input{
-			input,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0), op.Output(1)
-}
-
-// AudioSummaryAttr is an optional argument to AudioSummary.
-type AudioSummaryAttr func(optionalAttr)
-
-// AudioSummaryMaxOutputs sets the optional max_outputs attribute to value.
-//
-// value: Max number of batch elements to generate audio for.
-// If not specified, defaults to 3
-//
-// REQUIRES: value >= 1
-func AudioSummaryMaxOutputs(value int64) AudioSummaryAttr {
-	return func(m optionalAttr) {
-		m["max_outputs"] = value
-	}
-}
-
-// Outputs a `Summary` protocol buffer with audio.
-//
-// DEPRECATED at GraphDef version 15: Use AudioSummaryV2.
-//
-// The summary has up to `max_outputs` summary values containing audio. The
-// audio is built from `tensor` which must be 3-D with shape `[batch_size,
-// frames, channels]` or 2-D with shape `[batch_size, frames]`. The values are
-// assumed to be in the range of `[-1.0, 1.0]` with a sample rate of `sample_rate`.
-//
-// The `tag` argument is a scalar `Tensor` of type `string`.  It is used to
-// build the `tag` of the summary values:
-//
-// *  If `max_outputs` is 1, the summary value tag is '*tag*/audio'.
-// *  If `max_outputs` is greater than 1, the summary value tags are
-//    generated sequentially as '*tag*/audio/0', '*tag*/audio/1', etc.
-//
-// Arguments:
-//	tag: Scalar. Used to build the `tag` attribute of the summary values.
-//	tensor: 2-D of shape `[batch_size, frames]`.
-//	sample_rate: The sample rate of the signal in hertz.
-//
-// Returns Scalar. Serialized `Summary` protocol buffer.
-func AudioSummary(scope *Scope, tag tf.Output, tensor tf.Output, sample_rate float32, optional ...AudioSummaryAttr) (summary tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"sample_rate": sample_rate}
-	for _, a := range optional {
-		a(attrs)
-	}
-	opspec := tf.OpSpec{
-		Type: "AudioSummary",
-		Input: []tf.Input{
-			tag, tensor,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
 // Replaces the contents of the table with the specified keys and values.
 //
 // The tensor `keys` must be of the same type as the keys of the table.
@@ -26357,77 +26431,3 @@ func Inv(scope *Scope, x tf.Output) (y tf.Output) {
 	op := scope.AddOperation(opspec)
 	return op.Output(0)
 }
-
-// Returns x / y element-wise for integer types.
-//
-// Truncation designates that negative numbers will round fractional quantities
-// toward zero. I.e. -7 / 5 = 1. This matches C semantics but it is different
-// than Python semantics. See `FloorDiv` for a division function that matches
-// Python Semantics.
-//
-// *NOTE*: `TruncateDiv` supports broadcasting. More about broadcasting
-// [here](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)
-func TruncateDiv(scope *Scope, x tf.Output, y tf.Output) (z tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	opspec := tf.OpSpec{
-		Type: "TruncateDiv",
-		Input: []tf.Input{
-			x, y,
-		},
-	}
-	op := scope.AddOperation(opspec)
-	return op.Output(0)
-}
-
-// Restores tensors from a V2 checkpoint.
-//
-// For backward compatibility with the V1 format, this Op currently allows
-// restoring from a V1 checkpoint as well:
-//   - This Op first attempts to find the V2 index file pointed to by "prefix", and
-//     if found proceed to read it as a V2 checkpoint;
-//   - Otherwise the V1 read path is invoked.
-// Relying on this behavior is not recommended, as the ability to fall back to read
-// V1 might be deprecated and eventually removed.
-//
-// By default, restores the named tensors in full.  If the caller wishes to restore
-// specific slices of stored tensors, "shape_and_slices" should be non-empty
-// strings and correspondingly well-formed.
-//
-// Callers must ensure all the named tensors are indeed stored in the checkpoint.
-//
-// Arguments:
-//	prefix: Must have a single element.  The prefix of a V2 checkpoint.
-//	tensor_names: shape {N}.  The names of the tensors to be restored.
-//	shape_and_slices: shape {N}.  The slice specs of the tensors to be restored.
-// Empty strings indicate that they are non-partitioned tensors.
-//	dtypes: shape {N}.  The list of expected dtype for the tensors.  Must match
-// those stored in the checkpoint.
-//
-// Returns shape {N}.  The restored tensors, whose shapes are read from the
-// checkpoint directly.
-func RestoreV2(scope *Scope, prefix tf.Output, tensor_names tf.Output, shape_and_slices tf.Output, dtypes []tf.DataType) (tensors []tf.Output) {
-	if scope.Err() != nil {
-		return
-	}
-	attrs := map[string]interface{}{"dtypes": dtypes}
-	opspec := tf.OpSpec{
-		Type: "RestoreV2",
-		Input: []tf.Input{
-			prefix, tensor_names, shape_and_slices,
-		},
-		Attrs: attrs,
-	}
-	op := scope.AddOperation(opspec)
-	if scope.Err() != nil {
-		return
-	}
-	var idx int
-	var err error
-	if tensors, idx, err = makeOutputList(op, idx, "tensors"); err != nil {
-		scope.UpdateErr("RestoreV2", err)
-		return
-	}
-	return tensors
-}