Op

public interface Op

Known Indirect Subclasses

Abort, Abs<T extends TNumber>, AccumulateN<T extends TType>, AccumulatorApplyGradient, AccumulatorNumAccumulated, AccumulatorSetGlobalStep, AccumulatorTakeGradient<T extends TType>, Acos<T extends TType>, Acosh<T extends TType>, Add<T extends TType>, AddManySparseToTensorsMap, AddN<T extends TType>, AddSparseToTensorsMap, AdjustContrast<T extends TNumber>, AdjustHue<T extends TNumber>, and 1157 others.

Abort	Raise a exception to abort the process when called.
Abs<T extends TNumber>	Computes the absolute value of a tensor.
AccumulateN<T extends TType>	Returns the element-wise sum of a list of tensors.
AccumulatorApplyGradient	Applies a gradient to a given accumulator.
AccumulatorNumAccumulated	Returns the number of gradients aggregated in the given accumulators.
AccumulatorSetGlobalStep	Updates the accumulator with a new value for global_step.
AccumulatorTakeGradient<T extends TType>	Extracts the average gradient in the given ConditionalAccumulator.
Acos<T extends TType>	Computes acos of x element-wise.
Acosh<T extends TType>	Computes inverse hyperbolic cosine of x element-wise.
Add<T extends TType>	Returns x + y element-wise.
AddManySparseToTensorsMap	Add an `N`-minibatch `SparseTensor` to a `SparseTensorsMap`, return `N` handles.
AddN<T extends TType>	Add all input tensors element wise.
AddSparseToTensorsMap	Add a `SparseTensor` to a `SparseTensorsMap` return its handle.
AdjustContrast<T extends TNumber>	Adjust the contrast of one or more images.
AdjustHue<T extends TNumber>	Adjust the hue of one or more images.
AdjustSaturation<T extends TNumber>	Adjust the saturation of one or more images.
All	Computes the "logical and" of elements across dimensions of a tensor.
AllCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
AllReduce<T extends TNumber>	Mutually reduces multiple tensors of identical type and shape.
AllToAll<T extends TType>	An Op to exchange data across TPU replicas.
Angle<U extends TNumber>	Returns the argument of a complex number.
AnonymousIterator	A container for an iterator resource.
AnonymousMemoryCache
AnonymousMultiDeviceIterator	A container for a multi device iterator resource.
AnonymousRandomSeedGenerator
AnonymousSeedGenerator
Any	Computes the "logical or" of elements across dimensions of a tensor.
ApplyAdaMax<T extends TType>	Update '*var' according to the AdaMax algorithm.
ApplyAdadelta<T extends TType>	Update '*var' according to the adadelta scheme.
ApplyAdagrad<T extends TType>	Update '*var' according to the adagrad scheme.
ApplyAdagradDa<T extends TType>	Update '*var' according to the proximal adagrad scheme.
ApplyAdagradV2<T extends TType>	Update '*var' according to the adagrad scheme.
ApplyAdam<T extends TType>	Update '*var' according to the Adam algorithm.
ApplyAddSign<T extends TType>	Update '*var' according to the AddSign update.
ApplyCenteredRmsProp<T extends TType>	Update '*var' according to the centered RMSProp algorithm.
ApplyFtrl<T extends TType>	Update '*var' according to the Ftrl-proximal scheme.
ApplyGradientDescent<T extends TType>	Update 'var' by subtracting 'alpha' 'delta' from it.
ApplyMomentum<T extends TType>	Update '*var' according to the momentum scheme.
ApplyPowerSign<T extends TType>	Update '*var' according to the AddSign update.
ApplyProximalAdagrad<T extends TType>	Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
ApplyProximalGradientDescent<T extends TType>	Update '*var' as FOBOS algorithm with fixed learning rate.
ApplyRmsProp<T extends TType>	Update '*var' according to the RMSProp algorithm.
ApproximateEqual	Returns the truth value of abs(x-y) < tolerance element-wise.
ArgMax<V extends TNumber>	Returns the index with the largest value across dimensions of a tensor.
ArgMin<V extends TNumber>	Returns the index with the smallest value across dimensions of a tensor.
AsString	Converts each entry in the given tensor to strings.
Asin<T extends TType>	Computes the trignometric inverse sine of x element-wise.
Asinh<T extends TType>	Computes inverse hyperbolic sine of x element-wise.
AssertCardinalityDataset
AssertNextDataset
AssertThat	Asserts that the given condition is true.
Assign<T extends TType>	Update 'ref' by assigning 'value' to it.
AssignAdd<T extends TType>	Update 'ref' by adding 'value' to it.
AssignAddVariableOp	Adds a value to the current value of a variable.
AssignSub<T extends TType>	Update 'ref' by subtracting 'value' from it.
AssignSubVariableOp	Subtracts a value from the current value of a variable.
AssignVariableOp	Assigns a new value to a variable.
Atan<T extends TType>	Computes the trignometric inverse tangent of x element-wise.
Atan2<T extends TNumber>	Computes arctangent of `y/x` element-wise, respecting signs of the arguments.
Atanh<T extends TType>	Computes inverse hyperbolic tangent of x element-wise.
AudioSpectrogram	Produces a visualization of audio data over time.
AudioSummary	Outputs a `Summary` protocol buffer with audio.
AutoShardDataset	Creates a dataset that shards the input dataset.
AvgPool<T extends TNumber>	Performs average pooling on the input.
AvgPool3d<T extends TNumber>	Performs 3D average pooling on the input.
AvgPool3dGrad<T extends TNumber>	Computes gradients of average pooling function.
AvgPoolGrad<T extends TNumber>	Computes gradients of the average pooling function.
BandPart<T extends TType>	Copy a tensor setting everything outside a central band in each innermost matrix to zero.
BandedTriangularSolve<T extends TType>
Barrier	Defines a barrier that persists across different graph executions.
BarrierClose	Closes the given barrier.
BarrierIncompleteSize	Computes the number of incomplete elements in the given barrier.
BarrierInsertMany	For each key, assigns the respective value to the specified component.
BarrierReadySize	Computes the number of complete elements in the given barrier.
BarrierTakeMany	Takes the given number of completed elements from a barrier.
Batch	Batches all input tensors nondeterministically.
BatchCholesky<T extends TNumber>
BatchCholeskyGrad<T extends TNumber>
BatchDataset	Creates a dataset that batches `batch_size` elements from `input_dataset`.
BatchFft
BatchFft2d
BatchFft3d
BatchIfft
BatchIfft2d
BatchIfft3d
BatchMatMul<T extends TType>	Multiplies slices of two tensors in batches.
BatchMatrixBandPart<T extends TType>
BatchMatrixDeterminant<T extends TType>
BatchMatrixDiag<T extends TType>
BatchMatrixDiagPart<T extends TType>
BatchMatrixInverse<T extends TNumber>
BatchMatrixSetDiag<T extends TType>
BatchMatrixSolve<T extends TNumber>
BatchMatrixSolveLs<T extends TNumber>
BatchMatrixTriangularSolve<T extends TNumber>
BatchNormWithGlobalNormalization<T extends TType>	Batch normalization.
BatchNormWithGlobalNormalizationGrad<T extends TType>	Gradients for batch normalization.
BatchSelfAdjointEig<T extends TNumber>
BatchSvd<T extends TType>
BatchToSpace<T extends TType>	BatchToSpace for 4-D tensors of type T.
BatchToSpaceNd<T extends TType>	BatchToSpace for N-D tensors of type T.
BesselI0<T extends TNumber>
BesselI0e<T extends TNumber>
BesselI1<T extends TNumber>
BesselI1e<T extends TNumber>
BesselJ0<T extends TNumber>
BesselJ1<T extends TNumber>
BesselK0<T extends TNumber>
BesselK0e<T extends TNumber>
BesselK1<T extends TNumber>
BesselK1e<T extends TNumber>
BesselY0<T extends TNumber>
BesselY1<T extends TNumber>
Betainc<T extends TNumber>	Compute the regularized incomplete beta integral \\(I_x(a, b)\\).
BiasAdd<T extends TType>	Adds `bias` to `value`.
BiasAddGrad<T extends TType>	The backward operation for "BiasAdd" on the "bias" tensor.
Bincount<T extends TNumber>	Counts the number of occurrences of each value in an integer array.
Bitcast<U extends TType>	Bitcasts a tensor from one type to another without copying data.
BitwiseAnd<T extends TNumber>	Elementwise computes the bitwise AND of `x` and `y`.
BitwiseOr<T extends TNumber>	Elementwise computes the bitwise OR of `x` and `y`.
BitwiseXor<T extends TNumber>	Elementwise computes the bitwise XOR of `x` and `y`.
BlockLSTM<T extends TNumber>	Computes the LSTM cell forward propagation for all the time steps.
BlockLSTMGrad<T extends TNumber>	Computes the LSTM cell backward propagation for the entire time sequence.
BoostedTreesAggregateStats	Aggregates the summary of accumulated stats for the batch.
BoostedTreesBucketize	Bucketize each feature based on bucket boundaries.
BoostedTreesCalculateBestFeatureSplit	Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesCalculateBestFeatureSplitV2	Calculates gains for each feature and returns the best possible split information for each node.
BoostedTreesCalculateBestGainsPerFeature	Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesCenterBias	Calculates the prior from the training data (the bias) and fills in the first node with the logits' prior.
BoostedTreesCreateEnsemble	Creates a tree ensemble model and returns a handle to it.
BoostedTreesCreateQuantileStreamResource	Create the Resource for Quantile Streams.
BoostedTreesDeserializeEnsemble	Deserializes a serialized tree ensemble config and replaces current tree ensemble.
BoostedTreesEnsembleResourceHandleOp	Creates a handle to a BoostedTreesEnsembleResource
BoostedTreesExampleDebugOutputs	Debugging/model interpretability outputs for each example.
BoostedTreesFlushQuantileSummaries	Flush the quantile summaries from each quantile stream resource.
BoostedTreesGetEnsembleStates	Retrieves the tree ensemble resource stamp token, number of trees and growing statistics.
BoostedTreesMakeQuantileSummaries	Makes the summary of quantiles for the batch.
BoostedTreesMakeStatsSummary	Makes the summary of accumulated stats for the batch.
BoostedTreesPredict	Runs multiple additive regression ensemble predictors on input instances and computes the logits.
BoostedTreesQuantileStreamResourceAddSummaries	Add the quantile summaries to each quantile stream resource.
BoostedTreesQuantileStreamResourceDeserialize	Deserialize bucket boundaries and ready flag into current QuantileAccumulator.
BoostedTreesQuantileStreamResourceFlush	Flush the summaries for a quantile stream resource.
BoostedTreesQuantileStreamResourceGetBucketBoundaries	Generate the bucket boundaries for each feature based on accumulated summaries.
BoostedTreesQuantileStreamResourceHandleOp	Creates a handle to a BoostedTreesQuantileStreamResource.
BoostedTreesSerializeEnsemble	Serializes the tree ensemble to a proto.
BoostedTreesSparseAggregateStats	Aggregates the summary of accumulated stats for the batch.
BoostedTreesSparseCalculateBestFeatureSplit	Calculates gains for each feature and returns the best possible split information for the feature.
BoostedTreesTrainingPredict	Runs multiple additive regression ensemble predictors on input instances and computes the update to cached logits.
BoostedTreesUpdateEnsemble	Updates the tree ensemble by either adding a layer to the last tree being grown or by starting a new tree.
BoostedTreesUpdateEnsembleV2	Updates the tree ensemble by adding a layer to the last tree being grown or by starting a new tree.
BroadcastDynamicShape<T extends TNumber>	Return the shape of s0 op s1 with broadcast.
BroadcastGradientArgs<T extends TNumber>	Return the reduction indices for computing gradients of s0 op s1 with broadcast.
BroadcastHelper<T extends TType>	Helper operator for performing XLA-style broadcasts Broadcasts `lhs` and `rhs` to the same rank, by adding size 1 dimensions to whichever of `lhs` and `rhs` has the lower rank, using XLA's broadcasting rules for binary operators.
BroadcastRecv<T extends TType>	Receives a tensor value broadcast from another device.
BroadcastSend<T extends TType>	Broadcasts a tensor value to one or more other devices.
BroadcastTo<T extends TType>	Broadcast an array for a compatible shape.
Bucketize	Bucketizes 'input' based on 'boundaries'.
BytesProducedStatsDataset	Records the bytes size of each element of `input_dataset` in a StatsAggregator.
CSRSparseMatrixComponents<T extends TType>	Reads out the CSR components at batch `index`.
CSRSparseMatrixToDense<T extends TType>	Convert a (possibly batched) CSRSparseMatrix to dense.
CSRSparseMatrixToSparseTensor<T extends TType>	Converts a (possibly batched) CSRSparesMatrix to a SparseTensor.
CSVDataset
CSVDatasetV2
CTCLossV2	Calculates the CTC Loss (log probability) for each batch entry.
CacheDataset	Creates a dataset that caches elements from `input_dataset`.
CacheDatasetV2
Cast<U extends TType>	Cast x of type SrcT to y of DstT.
Ceil<T extends TNumber>	Returns element-wise smallest integer not less than x.
CheckNumerics<T extends TNumber>	Checks a tensor for NaN, -Inf and +Inf values.
Cholesky<T extends TType>	Computes the Cholesky decomposition of one or more square matrices.
CholeskyGrad<T extends TNumber>	Computes the reverse mode backpropagated gradient of the Cholesky algorithm.
ChooseFastestDataset
ClipByValue<T extends TType>	Clips tensor values to a specified min and max.
CloseSummaryWriter
ClusterOutput<T extends TType>	Operator that connects the output of an XLA computation to other consumer graph nodes.
CollectiveGather<T extends TNumber>	Mutually accumulates multiple tensors of identical type and shape.
CollectivePermute<T extends TType>	An Op to permute tensors across replicated TPU instances.
CombinedNonMaxSuppression	Greedily selects a subset of bounding boxes in descending order of score, This operation performs non_max_suppression on the inputs per batch, across all classes.
CompareAndBitpack	Compare values of `input` to `threshold` and pack resulting bits into a `uint8`.
CompilationResult	Returns the result of a TPU compilation.
CompileSucceededAssert	Asserts that compilation succeeded.
Complex<U extends TType>	Converts two real numbers to a complex number.
ComplexAbs<U extends TNumber>	Computes the complex absolute value of a tensor.
CompressElement	Compresses a dataset element.
ComputeAccidentalHits	Computes the ids of the positions in sampled_candidates that match true_labels.
ComputeBatchSize	Computes the static batch size of a dataset sans partial batches.
Concat<T extends TType>	Concatenates tensors along one dimension.
ConcatenateDataset	Creates a dataset that concatenates `input_dataset` with `another_dataset`.
ConditionalAccumulator	A conditional accumulator for aggregating gradients.
ConfigureDistributedTPU	Sets up the centralized structures for a distributed TPU system.
ConfigureTPUEmbedding	Sets up TPUEmbedding in a distributed TPU system.
Conj<T extends TType>	Returns the complex conjugate of a complex number.
ConjugateTranspose<T extends TType>	Shuffle dimensions of x according to a permutation and conjugate the result.
Constant<T extends TType>	An operator producing a constant value.
ConsumeMutexLock	This op consumes a lock created by `MutexLock`.
ControlTrigger	Does nothing.
Conv<T extends TType>	Wraps the XLA ConvGeneralDilated operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#conv_convolution .
Conv2d<T extends TNumber>	Computes a 2-D convolution given 4-D `input` and `filter` tensors.
Conv2dBackpropFilter<T extends TNumber>	Computes the gradients of convolution with respect to the filter.
Conv2dBackpropInput<T extends TNumber>	Computes the gradients of convolution with respect to the input.
Conv3d<T extends TNumber>	Computes a 3-D convolution given 5-D `input` and `filter` tensors.
Conv3dBackpropFilter<T extends TNumber>	Computes the gradients of 3-D convolution with respect to the filter.
Conv3dBackpropInput<U extends TNumber>	Computes the gradients of 3-D convolution with respect to the input.
Copy<T extends TType>	Copy a tensor from CPU-to-CPU or GPU-to-GPU.
CopyHost<T extends TType>	Copy a tensor to host.
Cos<T extends TType>	Computes cos of x element-wise.
Cosh<T extends TType>	Computes hyperbolic cosine of x element-wise.
CountUpTo<T extends TNumber>	Increments 'ref' until it reaches 'limit'.
CreateSummaryDbWriter
CreateSummaryFileWriter
CropAndResize	Extracts crops from the input image tensor and resizes them.
CropAndResizeGradBoxes	Computes the gradient of the crop_and_resize op wrt the input boxes tensor.
CropAndResizeGradImage<T extends TNumber>	Computes the gradient of the crop_and_resize op wrt the input image tensor.
Cross<T extends TNumber>	Compute the pairwise cross product.
CrossReplicaSum<T extends TNumber>	An Op to sum inputs across replicated TPU instances.
CtcBeamSearchDecoder<T extends TNumber>	Performs beam search decoding on the logits given in input.
CtcGreedyDecoder<T extends TNumber>	Performs greedy decoding on the logits given in inputs.
CtcLoss<T extends TNumber>	Calculates the CTC Loss (log probability) for each batch entry.
CudnnRNN<T extends TNumber>	A RNN backed by cuDNN.
CudnnRNNBackprop<T extends TNumber>	Backprop step of CudnnRNNV3.
CudnnRNNCanonicalToParams<T extends TNumber>	Converts CudnnRNN params from canonical form to usable form.
CudnnRNNParamsToCanonical<T extends TNumber>	Retrieves CudnnRNN params in canonical form.
CudnnRnnParamsSize<U extends TNumber>	Computes size of weights that can be used by a Cudnn RNN model.
Cumprod<T extends TType>	Compute the cumulative product of the tensor `x` along `axis`.
Cumsum<T extends TType>	Compute the cumulative sum of the tensor `x` along `axis`.
CumulativeLogsumexp<T extends TNumber>	Compute the cumulative product of the tensor `x` along `axis`.
DataFormatDimMap<T extends TNumber>	Returns the dimension index in the destination data format given the one in the source data format.
DataFormatVecPermute<T extends TNumber>	Permute input tensor from `src_format` to `dst_format`.
DataServiceDataset
DatasetCardinality	Returns the cardinality of `input_dataset`.
DatasetFromGraph	Creates a dataset from the given `graph_def`.
DatasetToGraph	Returns a serialized GraphDef representing `input_dataset`.
DatasetToSingleElement	Outputs the single element from the given dataset.
DatasetToTFRecord	Writes the given dataset to the given file using the TFRecord format.
DatasetToTfRecord	Writes the given dataset to the given file using the TFRecord format.
Dawsn<T extends TNumber>
DebugGradientIdentity<T extends TType>	Identity op for gradient debugging.
DebugGradientRefIdentity<T extends TType>	Identity op for gradient debugging.
DebugIdentity<T extends TType>	Debug Identity V2 Op.
DebugNanCount	Debug NaN Value Counter Op.
DebugNumericsSummary<U extends TNumber>	Debug Numeric Summary V2 Op.
DecodeAndCropJpeg	Decode and Crop a JPEG-encoded image to a uint8 tensor.
DecodeBase64	Decode web-safe base64-encoded strings.
DecodeBmp	Decode the first frame of a BMP-encoded image to a uint8 tensor.
DecodeCompressed	Decompress strings.
DecodeCsv	Convert CSV records to tensors.
DecodeGif	Decode the frame(s) of a GIF-encoded image to a uint8 tensor.
DecodeImage<T extends TNumber>	Function for decode_bmp, decode_gif, decode_jpeg, and decode_png.
DecodeJpeg	Decode a JPEG-encoded image to a uint8 tensor.
DecodeJsonExample	Convert JSON-encoded Example records to binary protocol buffer strings.
DecodePaddedRaw<T extends TNumber>	Reinterpret the bytes of a string as a vector of numbers.
DecodePng<T extends TNumber>	Decode a PNG-encoded image to a uint8 or uint16 tensor.
DecodeProto	The op extracts fields from a serialized protocol buffers message into tensors.
DecodeRaw<T extends TType>	Reinterpret the bytes of a string as a vector of numbers.
DecodeWav	Decode a 16-bit PCM WAV file to a float tensor.
DeepCopy<T extends TType>	Makes a copy of `x`.
DeleteIterator	A container for an iterator resource.
DeleteMemoryCache
DeleteMultiDeviceIterator	A container for an iterator resource.
DeleteRandomSeedGenerator
DeleteSeedGenerator
DeleteSessionTensor	Delete the tensor specified by its handle in the session.
DenseBincount<U extends TNumber>	Counts the number of occurrences of each value in an integer array.
DenseCountSparseOutput<U extends TNumber>	Performs sparse-output bin counting for a tf.tensor input.
DenseToCSRSparseMatrix	Converts a dense tensor to a (possibly batched) CSRSparseMatrix.
DenseToDenseSetOperation<T extends TType>	Applies set operation along last dimension of 2 `Tensor` inputs.
DenseToSparseBatchDataset	Creates a dataset that batches input elements into a SparseTensor.
DenseToSparseSetOperation<T extends TType>	Applies set operation along last dimension of `Tensor` and `SparseTensor`.
DepthToSpace<T extends TType>	DepthToSpace for tensors of type T.
DepthwiseConv2dNative<T extends TNumber>	Computes a 2-D depthwise convolution given 4-D `input` and `filter` tensors.
DepthwiseConv2dNativeBackpropFilter<T extends TNumber>	Computes the gradients of depthwise convolution with respect to the filter.
DepthwiseConv2dNativeBackpropInput<T extends TNumber>	Computes the gradients of depthwise convolution with respect to the input.
Dequantize	Takes the packed uint32 input and unpacks the input to uint8 to do Dequantization on device.
DeserializeIterator	Converts the given variant tensor to an iterator and stores it in the given resource.
DeserializeManySparse<T extends TType>	Deserialize and concatenate `SparseTensors` from a serialized minibatch.
DeserializeSparse<U extends TType>	Deserialize `SparseTensor` objects.
DestroyResourceOp	Deletes the resource specified by the handle.
DestroyTemporaryVariable<T extends TType>	Destroys the temporary variable and returns its final value.
Det<T extends TType>	Computes the determinant of one or more square matrices.
DeviceIndex	Return the index of device the op runs.
Digamma<T extends TNumber>	Computes Psi, the derivative of Lgamma (the log of the absolute value of `Gamma(x)`), element-wise.
Dilation2d<T extends TNumber>	Computes the grayscale dilation of 4-D `input` and 3-D `filter` tensors.
Dilation2dBackpropFilter<T extends TNumber>	Computes the gradient of morphological 2-D dilation with respect to the filter.
Dilation2dBackpropInput<T extends TNumber>	Computes the gradient of morphological 2-D dilation with respect to the input.
DirectedInterleaveDataset	A substitute for `InterleaveDataset` on a fixed list of `N` datasets.
Div<T extends TType>	Returns x / y element-wise.
DivNoNan<T extends TType>	Returns 0 if the denominator is zero.
Dot<T extends TType>	Wraps the XLA DotGeneral operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#dotgeneral .
DrawBoundingBoxes<T extends TNumber>	Draw bounding boxes on a batch of images.
DummyIterationCounter
DummyMemoryCache
DummySeedGenerator
DynamicPartition<T extends TType>	Partitions `data` into `num_partitions` tensors using indices from `partitions`.
DynamicSlice<T extends TType>	Wraps the XLA DynamicSlice operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#dynamicslice .
DynamicStitch<T extends TType>	Interleave the values from the `data` tensors into a single tensor.
DynamicUpdateSlice<T extends TType>	Wraps the XLA DynamicUpdateSlice operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#dynamicupdateslice .
EditDistance	Computes the (possibly normalized) Levenshtein Edit Distance.
Eig<U extends TType>	Computes the eigen decomposition of one or more square matrices.
Einsum<T extends TType>	An op which supports basic einsum op with 2 inputs and 1 output.
Elu<T extends TNumber>	Computes exponential linear: `exp(features) - 1` if < 0, `features` otherwise.
EluGrad<T extends TNumber>	Computes gradients for the exponential linear (Elu) operation.
EmbeddingActivations	An op enabling differentiation of TPU Embeddings.
Empty<T extends TType>	Creates a tensor with the given shape.
EmptyTensorList	Creates and returns an empty tensor list.
EmptyTensorMap	Creates and returns an empty tensor map.
EncodeBase64	Encode strings into web-safe base64 format.
EncodeJpeg	JPEG-encode an image.
EncodeJpegVariableQuality	JPEG encode input image with provided compression quality.
EncodePng	PNG-encode an image.
EncodeProto	The op serializes protobuf messages provided in the input tensors.
EncodeWav	Encode audio data using the WAV file format.
EnqueueTPUEmbeddingIntegerBatch	An op that enqueues a list of input batch tensors to TPUEmbedding.
EnqueueTPUEmbeddingRaggedTensorBatch	Eases the porting of code that uses tf.nn.embedding_lookup().
EnqueueTPUEmbeddingSparseBatch	An op that enqueues TPUEmbedding input indices from a SparseTensor.
EnqueueTPUEmbeddingSparseTensorBatch	Eases the porting of code that uses tf.nn.embedding_lookup_sparse().
EnsureShape<T extends TType>	Ensures that the tensor's shape matches the expected shape.
Enter<T extends TType>	Creates or finds a child frame, and makes `data` available to the child frame.
Equal	Returns the truth value of (x == y) element-wise.
Erf<T extends TNumber>	Computes the Gauss error function of `x` element-wise.
Erfc<T extends TNumber>	Computes the complementary error function of `x` element-wise.
EuclideanNorm<T extends TType>	Computes the euclidean norm of elements across dimensions of a tensor.
Execute	Op that loads and executes a TPU program on a TPU device.
ExecuteAndUpdateVariables	Op that executes a program with optional in-place variable updates.
Exit<T extends TType>	Exits the current frame to its parent frame.
Exp<T extends TType>	Computes exponential of x element-wise.
ExpandDims<T extends TType>	Inserts a dimension of 1 into a tensor's shape.
Expint<T extends TNumber>
Expm1<T extends TType>	Computes `exp(x) - 1` element-wise.
ExtractGlimpse	Extracts a glimpse from the input tensor.
ExtractImagePatches<T extends TType>	Extract `patches` from `images` and put them in the "depth" output dimension.
ExtractJpegShape<T extends TNumber>	Extract the shape information of a JPEG-encoded image.
ExtractVolumePatches<T extends TNumber>	Extract `patches` from `input` and put them in the `"depth"` output dimension.
Fact	Output a fact about factorials.
FakeQuantWithMinMaxArgs	Fake-quantize the 'inputs' tensor, type float to 'outputs' tensor of same type.
FakeQuantWithMinMaxArgsGradient	Compute gradients for a FakeQuantWithMinMaxArgs operation.
FakeQuantWithMinMaxVars	Fake-quantize the 'inputs' tensor of type float via global float scalars Fake-quantize the `inputs` tensor of type float via global float scalars `min` and `max` to `outputs` tensor of same shape as `inputs`.
FakeQuantWithMinMaxVarsGradient	Compute gradients for a FakeQuantWithMinMaxVars operation.
FakeQuantWithMinMaxVarsPerChannel	Fake-quantize the 'inputs' tensor of type float via per-channel floats Fake-quantize the `inputs` tensor of type float per-channel and one of the shapes: `[d]`, `[b, d]` `[b, h, w, d]` via per-channel floats `min` and `max` of shape `[d]` to `outputs` tensor of same shape as `inputs`.
FakeQuantWithMinMaxVarsPerChannelGradient	Compute gradients for a FakeQuantWithMinMaxVarsPerChannel operation.
Fft<T extends TType>	Fast Fourier transform.
Fft2d<T extends TType>	2D fast Fourier transform.
Fft3d<T extends TType>	3D fast Fourier transform.
FifoQueue	A queue that produces elements in first-in first-out order.
Fill<U extends TType>	Creates a tensor filled with a scalar value.
FilterByLastComponentDataset	Creates a dataset containing elements of first component of `input_dataset` having true in the last component.
Fingerprint	Generates fingerprint values.
FixedLengthRecordDataset
FixedLengthRecordReader	A Reader that outputs fixed-length records from a file.
FixedUnigramCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
Floor<T extends TNumber>	Returns element-wise largest integer not greater than x.
FloorDiv<T extends TType>	Returns x // y element-wise.
FloorMod<T extends TNumber>	Returns element-wise remainder of division.
FlushSummaryWriter
FractionalAvgPool<T extends TNumber>	Performs fractional average pooling on the input.
FractionalAvgPoolGrad<T extends TNumber>	Computes gradient of the FractionalAvgPool function.
FractionalMaxPool<T extends TNumber>	Performs fractional max pooling on the input.
FractionalMaxPoolGrad<T extends TNumber>	Computes gradient of the FractionalMaxPool function.
FresnelCos<T extends TNumber>
FresnelSin<T extends TNumber>
FusedBatchNorm<T extends TNumber, U extends TNumber>	Batch normalization.
FusedBatchNormGrad<T extends TNumber, U extends TNumber>	Gradient for batch normalization.
FusedPadConv2d<T extends TNumber>	Performs a padding as a preprocess during a convolution.
FusedResizeAndPadConv2d<T extends TNumber>	Performs a resize and padding as a preprocess during a convolution.
GRUBlockCell<T extends TNumber>	Computes the GRU cell forward propagation for 1 time step.
GRUBlockCellGrad<T extends TNumber>	Computes the GRU cell back-propagation for 1 time step.
Gather<T extends TType>	Wraps the XLA Gather operator documented at https://www.tensorflow.org/xla/operation_semantics#gather
GatherNd<T extends TType>	Gather slices from `params` into a Tensor with shape specified by `indices`.
GatherV2<T extends TNumber>	Mutually accumulates multiple tensors of identical type and shape.
GenerateBoundingBoxProposals	This op produces Region of Interests from given bounding boxes(bbox_deltas) encoded wrt anchors according to eq.2 in arXiv:1506.01497 The op selects top `pre_nms_topn` scoring boxes, decodes them with respect to anchors, applies non-maximal suppression on overlapping boxes with higher than `nms_threshold` intersection-over-union (iou) value, discarding boxes where shorter side is less than `min_size`.
GenerateVocabRemapping	Given a path to new and old vocabulary files, returns a remapping Tensor of length `num_new_vocab`, where `remapping[i]` contains the row number in the old vocabulary that corresponds to row `i` in the new vocabulary (starting at line `new_vocab_offset` and up to `num_new_vocab` entities), or `-1` if entry `i` in the new vocabulary is not in the old vocabulary.
GetSessionHandle	Store the input tensor in the state of the current session.
GetSessionTensor<T extends TType>	Get the value of the tensor specified by its handle.
Greater	Returns the truth value of (x > y) element-wise.
GreaterEqual	Returns the truth value of (x >= y) element-wise.
GuaranteeConst<T extends TType>	Gives a guarantee to the TF runtime that the input tensor is a constant.
HashTable	Creates a non-initialized hash table.
HistogramFixedWidth<U extends TNumber>	Return histogram of values.
HistogramSummary	Outputs a `Summary` protocol buffer with a histogram.
HsvToRgb<T extends TNumber>	Convert one or more images from HSV to RGB.
Identity<T extends TType>	Return a tensor with the same shape and contents as the input tensor or value.
IdentityN	Returns a list of tensors with the same shapes and contents as the input tensors.
IdentityReader	A Reader that outputs the queued work as both the key and value.
Ifft<T extends TType>	Inverse fast Fourier transform.
Ifft2d<T extends TType>	Inverse 2D fast Fourier transform.
Ifft3d<T extends TType>	Inverse 3D fast Fourier transform.
Igamma<T extends TNumber>	Compute the lower regularized incomplete Gamma function `P(a, x)`.
IgammaGradA<T extends TNumber>	Computes the gradient of `igamma(a, x)` wrt `a`.
Igammac<T extends TNumber>	Compute the upper regularized incomplete Gamma function `Q(a, x)`.
IgnoreErrorsDataset	Creates a dataset that contains the elements of `input_dataset` ignoring errors.
Imag<U extends TNumber>	Returns the imaginary part of a complex number.
ImageProjectiveTransformV2<T extends TNumber>	Applies the given transform to each of the images.
ImageProjectiveTransformV3<T extends TNumber>	Applies the given transform to each of the images.
ImageSummary	Outputs a `Summary` protocol buffer with images.
ImmutableConst<T extends TType>	Returns immutable tensor from memory region.
ImportEvent
InTopK	Says whether the targets are in the top `K` predictions.
InfeedDequeue<T extends TType>	A placeholder op for a value that will be fed into the computation.
InfeedDequeueTuple	Fetches multiple values from infeed as an XLA tuple.
InfeedEnqueue	An op which feeds a single Tensor value into the computation.
InfeedEnqueuePrelinearizedBuffer	An op which enqueues prelinearized buffer into TPU infeed.
InfeedEnqueueTuple	Feeds multiple Tensor values into the computation as an XLA tuple.
Init
InitializeTable	Table initializer that takes two tensors for keys and values respectively.
InitializeTableFromDataset
InitializeTableFromTextFile	Initializes a table from a text file.
InplaceAdd<T extends TType>	Adds v into specified rows of x.
InplaceSub<T extends TType>	Subtracts `v` into specified rows of `x`.
InplaceUpdate<T extends TType>	Updates specified rows 'i' with values 'v'.
Inv<T extends TType>	Computes the inverse of one or more square invertible matrices or their adjoints (conjugate transposes).
InvGrad<T extends TType>	Computes the gradient for the inverse of `x` wrt its input.
Invert<T extends TNumber>	Invert (flip) each bit of supported types; for example, type `uint8` value 01010101 becomes 10101010.
InvertPermutation<T extends TNumber>	Computes the inverse permutation of a tensor.
Irfft<U extends TNumber>	Inverse real-valued fast Fourier transform.
Irfft2d<U extends TNumber>	Inverse 2D real-valued fast Fourier transform.
Irfft3d<U extends TNumber>	Inverse 3D real-valued fast Fourier transform.
IsBoostedTreesEnsembleInitialized	Checks whether a tree ensemble has been initialized.
IsBoostedTreesQuantileStreamResourceInitialized	Checks whether a quantile stream has been initialized.
IsFinite	Returns which elements of x are finite.
IsInf	Returns which elements of x are Inf.
IsNan	Returns which elements of x are NaN.
IsVariableInitialized	Checks whether a tensor has been initialized.
IsotonicRegression<U extends TNumber>	Solves a batch of isotonic regression problems.
Iterator
IteratorFromStringHandle
IteratorGetDevice	Returns the name of the device on which `resource` has been placed.
IteratorGetNext	Gets the next output from the given iterator .
IteratorGetNextAsOptional	Gets the next output from the given iterator as an Optional variant.
IteratorGetNextSync	Gets the next output from the given iterator.
IteratorToStringHandle	Converts the given `resource_handle` representing an iterator to a string.
Join	Joins the strings in the given list of string tensors into one tensor; with the given separator (default is an empty separator).
KMC2ChainInitialization	Returns the index of a data point that should be added to the seed set.
KeyValueSort<T extends TNumber, U extends TType>	Wraps the XLA Sort operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#sort .
KmeansPlusPlusInitialization	Selects num_to_sample rows of input using the KMeans++ criterion.
KthOrderStatistic	Computes the Kth order statistic of a data set.
L2Loss<T extends TNumber>	L2 Loss.
LMDBDataset	Creates a dataset that emits the key-value pairs in one or more LMDB files.
LSTMBlockCell<T extends TNumber>	Computes the LSTM cell forward propagation for 1 time step.
LSTMBlockCellGrad<T extends TNumber>	Computes the LSTM cell backward propagation for 1 timestep.
LatencyStatsDataset	Records the latency of producing `input_dataset` elements in a StatsAggregator.
LeakyRelu<T extends TNumber>	Computes rectified linear: `max(features, features * alpha)`.
LeakyReluGrad<T extends TNumber>	Computes rectified linear gradients for a LeakyRelu operation.
LearnedUnigramCandidateSampler	Generates labels for candidate sampling with a learned unigram distribution.
LeftShift<T extends TNumber>	Elementwise computes the bitwise left-shift of `x` and `y`.
Less	Returns the truth value of (x < y) element-wise.
LessEqual	Returns the truth value of (x <= y) element-wise.
Lgamma<T extends TNumber>	Computes the log of the absolute value of `Gamma(x)` element-wise.
LinSpace<T extends TNumber>	Generates values in an interval.
LmdbDataset
LmdbReader	A Reader that outputs the records from a LMDB file.
LoadAndRemapMatrix	Loads a 2-D (matrix) `Tensor` with name `old_tensor_name` from the checkpoint at `ckpt_path` and potentially reorders its rows and columns using the specified remappings.
LoadTPUEmbeddingADAMParameters	Load ADAM embedding parameters.
LoadTPUEmbeddingADAMParametersGradAccumDebug	Load ADAM embedding parameters with debug support.
LoadTPUEmbeddingAdadeltaParameters	Load Adadelta embedding parameters.
LoadTPUEmbeddingAdadeltaParametersGradAccumDebug	Load Adadelta parameters with debug support.
LoadTPUEmbeddingAdagradParameters	Load Adagrad embedding parameters.
LoadTPUEmbeddingAdagradParametersGradAccumDebug	Load Adagrad embedding parameters with debug support.
LoadTPUEmbeddingCenteredRMSPropParameters	Load centered RMSProp embedding parameters.
LoadTPUEmbeddingFTRLParameters	Load FTRL embedding parameters.
LoadTPUEmbeddingFTRLParametersGradAccumDebug	Load FTRL embedding parameters with debug support.
LoadTPUEmbeddingMDLAdagradLightParameters	Load MDL Adagrad Light embedding parameters.
LoadTPUEmbeddingMomentumParameters	Load Momentum embedding parameters.
LoadTPUEmbeddingMomentumParametersGradAccumDebug	Load Momentum embedding parameters with debug support.
LoadTPUEmbeddingProximalAdagradParameters	Load proximal Adagrad embedding parameters.
LoadTPUEmbeddingProximalAdagradParametersGradAccumDebug	Load proximal Adagrad embedding parameters with debug support.
LoadTPUEmbeddingProximalYogiParameters
LoadTPUEmbeddingProximalYogiParametersGradAccumDebug
LoadTPUEmbeddingRMSPropParameters	Load RMSProp embedding parameters.
LoadTPUEmbeddingRMSPropParametersGradAccumDebug	Load RMSProp embedding parameters with debug support.
LoadTPUEmbeddingStochasticGradientDescentParameters	Load SGD embedding parameters.
LoadTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug	Load SGD embedding parameters.
LocalResponseNormalization<T extends TNumber>	Local Response Normalization.
LocalResponseNormalizationGrad<T extends TNumber>	Gradients for Local Response Normalization.
Log<T extends TType>	Computes natural logarithm of x element-wise.
Log1p<T extends TType>	Computes natural logarithm of (1 + x) element-wise.
LogMatrixDeterminant<T extends TType>	Computes the sign and the log of the absolute value of the determinant of one or more square matrices.
LogSoftmax<T extends TNumber>	Computes log softmax activations.
LogUniformCandidateSampler	Generates labels for candidate sampling with a log-uniform distribution.
LogicalAnd	Returns the truth value of x AND y element-wise.
LogicalNot	Returns the truth value of `NOT x` element-wise.
LogicalOr	Returns the truth value of x OR y element-wise.
LookupTableExport<T extends TType, U extends TType>	Outputs all keys and values in the table.
LookupTableFind<U extends TType>	Looks up keys in a table, outputs the corresponding values.
LookupTableImport	Replaces the contents of the table with the specified keys and values.
LookupTableInsert	Updates the table to associates keys with values.
LookupTableRemove	Removes keys and its associated values from a table.
LookupTableSize	Computes the number of elements in the given table.
LoopCond	Forwards the input to the output.
Lower	Converts all uppercase characters into their respective lowercase replacements.
LowerBound<U extends TNumber>	Applies lower_bound(sorted_search_values, values) along each row.
Lu<T extends TType, U extends TNumber>	Computes the LU decomposition of one or more square matrices.
MakeIterator	Makes a new iterator from the given `dataset` and stores it in `iterator`.
MakeUnique	Make all elements in the non-Batch dimension unique, but \"close\" to their initial value.
MapClear	Op removes all elements in the underlying container.
MapIncompleteSize	Op returns the number of incomplete elements in the underlying container.
MapPeek	Op peeks at the values at the specified key.
MapSize	Op returns the number of elements in the underlying container.
MapStage	Stage (key, values) in the underlying container which behaves like a hashtable.
MapUnstage	Op removes and returns the values associated with the key from the underlying container.
MapUnstageNoKey	Op removes and returns a random (key, value) from the underlying container.
MatMul<T extends TType>	Multiply the matrix "a" by the matrix "b".
MatchingFiles	Returns the set of files matching one or more glob patterns.
MatchingFilesDataset
MatrixDiag<T extends TType>	Returns a batched diagonal tensor with given batched diagonal values.
MatrixDiagPart<T extends TType>	Returns the batched diagonal part of a batched tensor.
MatrixDiagPartV3<T extends TType>	Returns the batched diagonal part of a batched tensor.
MatrixDiagV3<T extends TType>	Returns a batched diagonal tensor with given batched diagonal values.
MatrixLogarithm<T extends TType>	Computes the matrix logarithm of one or more square matrices: \\(log(exp(A)) = A\\) This op is only defined for complex matrices.
MatrixSetDiag<T extends TType>	Returns a batched matrix tensor with new batched diagonal values.
MatrixSolveLs<T extends TType>	Solves one or more linear least-squares problems.
Max<T extends TType>	Computes the maximum of elements across dimensions of a tensor.
MaxIntraOpParallelismDataset	Creates a dataset that overrides the maximum intra-op parallelism.
MaxPool<T extends TType>	Performs max pooling on the input.
MaxPool3d<T extends TNumber>	Performs 3D max pooling on the input.
MaxPool3dGrad<U extends TNumber>	Computes gradients of 3D max pooling function.
MaxPool3dGradGrad<T extends TNumber>	Computes second-order gradients of the maxpooling function.
MaxPoolGrad<T extends TNumber>	Computes gradients of the maxpooling function.
MaxPoolGradGrad<T extends TNumber>	Computes second-order gradients of the maxpooling function.
MaxPoolGradGradWithArgmax<T extends TNumber>	Computes second-order gradients of the maxpooling function.
MaxPoolGradWithArgmax<T extends TNumber>	Computes gradients of the maxpooling function.
MaxPoolWithArgmax<T extends TNumber, U extends TNumber>	Performs max pooling on the input and outputs both max values and indices.
Maximum<T extends TNumber>	Returns the max of x and y (i.e.
Mean<T extends TType>	Computes the mean of elements across dimensions of a tensor.
Merge<T extends TType>	Forwards the value of an available tensor from `inputs` to `output`.
MergeSummary	Merges summaries.
MergeV2Checkpoints	V2 format specific: merges the metadata files of sharded checkpoints.
Mfcc	Transforms a spectrogram into a form that's useful for speech recognition.
Min<T extends TType>	Computes the minimum of elements across dimensions of a tensor.
Minimum<T extends TNumber>	Returns the min of x and y (i.e.
MirrorPad<T extends TType>	Pads a tensor with mirrored values.
MirrorPadGrad<T extends TType>	Gradient op for `MirrorPad` op.
MlirPassthroughOp	Wraps an arbitrary MLIR computation expressed as a module with a main() function.
Mod<T extends TNumber>	Returns element-wise remainder of division.
ModelDataset	Identity transformation that models performance.
Mul<T extends TType>	Returns x * y element-wise.
MulNoNan<T extends TType>	Returns x * y element-wise.
MultiDeviceIterator	Creates a MultiDeviceIterator resource.
MultiDeviceIteratorFromStringHandle	Generates a MultiDeviceIterator resource from its provided string handle.
MultiDeviceIteratorGetNextFromShard	Gets next element for the provided shard number.
MultiDeviceIteratorInit	Initializes the multi device iterator with the given dataset.
MultiDeviceIteratorToStringHandle	Produces a string handle for the given MultiDeviceIterator.
Multinomial<U extends TNumber>	Draws samples from a multinomial distribution.
MutableDenseHashTable	Creates an empty hash table that uses tensors as the backing store.
MutableHashTable	Creates an empty hash table.
MutableHashTableOfTensors	Creates an empty hash table.
Mutex	Creates a Mutex resource that can be locked by `MutexLock`.
MutexLock	Locks a mutex resource.
NcclAllReduce<T extends TNumber>	Outputs a tensor containing the reduction across all input tensors.
NcclBroadcast<T extends TNumber>	Sends `input` to all devices that are connected to the output.
NcclReduce<T extends TNumber>	Reduces `input` from `num_devices` using `reduction` to a single device.
Ndtri<T extends TNumber>
NearestNeighbors	Selects the k nearest centers for each point.
Neg<T extends TType>	Computes numerical negative value element-wise.
NegTrain	Training via negative sampling.
NextAfter<T extends TNumber>	Returns the next representable value of `x1` in the direction of `x2`, element-wise.
NextIteration<T extends TType>	Makes its input available to the next iteration.
NoOp	Does nothing.
NonDeterministicInts<U extends TType>	Non-deterministically generates some integers.
NonMaxSuppression<T extends TNumber>	Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high intersection-over-union (IOU) overlap with previously selected boxes.
NonMaxSuppressionWithOverlaps	Greedily selects a subset of bounding boxes in descending order of score, pruning away boxes that have high overlaps with previously selected boxes.
NonSerializableDataset
NotEqual	Returns the truth value of (x != y) element-wise.
NthElement<T extends TNumber>	Finds values of the `n`-th order statistic for the last dimension.
OneHot<U extends TType>	Returns a one-hot tensor.
Ones<T extends TType>	An operator creating a constant initialized with ones of the shape given by `dims`.
OnesLike<T extends TType>	Returns a tensor of ones with the same shape and type as x.
Operand<T extends TType>	Interface implemented by operands of a TensorFlow operation.
OptimizeDataset	Creates a dataset by applying optimizations to `input_dataset`.
OptimizeDatasetV2	Creates a dataset by applying related optimizations to `input_dataset`.
OptionalFromValue	Constructs an Optional variant from a tuple of tensors.
OptionalGetValue	Returns the value stored in an Optional variant or raises an error if none exists.
OptionalHasValue	Returns true if and only if the given Optional variant has a value.
OptionalNone	Creates an Optional variant with no value.
OrderedMapClear	Op removes all elements in the underlying container.
OrderedMapIncompleteSize	Op returns the number of incomplete elements in the underlying container.
OrderedMapPeek	Op peeks at the values at the specified key.
OrderedMapSize	Op returns the number of elements in the underlying container.
OrderedMapStage	Stage (key, values) in the underlying container which behaves like a ordered associative container.
OrderedMapUnstage	Op removes and returns the values associated with the key from the underlying container.
OrderedMapUnstageNoKey	Op removes and returns the (key, value) element with the smallest key from the underlying container.
OrdinalSelector	A TPU core selector Op.
OutfeedDequeue<T extends TType>	Retrieves a single tensor from the computation outfeed.
OutfeedDequeueTuple	Retrieve multiple values from the computation outfeed.
OutfeedDequeueTupleV2	Retrieve multiple values from the computation outfeed.
OutfeedDequeueV2<T extends TType>	Retrieves a single tensor from the computation outfeed.
OutfeedEnqueue	Enqueue a Tensor on the computation outfeed.
OutfeedEnqueueTuple	Enqueue multiple Tensor values on the computation outfeed.
Output<T extends TType>	A symbolic handle to a tensor produced by an `Operation`.
Pad<T extends TType>	Wraps the XLA Pad operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#pad .
PaddedBatchDataset	Creates a dataset that batches and pads `batch_size` elements from the input.
PaddingFifoQueue	A queue that produces elements in first-in first-out order.
ParallelConcat<T extends TType>	Concatenates a list of `N` tensors along the first dimension.
ParallelDynamicStitch<T extends TType>	Interleave the values from the `data` tensors into a single tensor.
ParameterizedTruncatedNormal<U extends TNumber>	Outputs random values from a normal distribution.
ParseExample	Transforms a vector of tf.Example protos (as strings) into typed tensors.
ParseExampleDataset	Transforms `input_dataset` containing `Example` protos as vectors of DT_STRING into a dataset of `Tensor` or `SparseTensor` objects representing the parsed features.
ParseSequenceExample	Transforms a vector of tf.io.SequenceExample protos (as strings) into typed tensors.
ParseSingleExample	Transforms a tf.Example proto (as a string) into typed tensors.
ParseSingleSequenceExample	Transforms a scalar brain.SequenceExample proto (as strings) into typed tensors.
ParseTensor<T extends TType>	Transforms a serialized tensorflow.TensorProto proto into a Tensor.
PartitionedInput<T extends TType>	An op that groups a list of partitioned inputs together.
PartitionedOutput<T extends TType>	An op that demultiplexes a tensor to be sharded by XLA to a list of partitioned outputs outside the XLA computation.
Placeholder<T extends TType>	A placeholder op for a value that will be fed into the computation.
PlaceholderWithDefault<T extends TType>	A placeholder op that passes through `input` when its output is not fed.
Polygamma<T extends TNumber>	Compute the polygamma function \\(\psi^{(n)}(x)\\).
PopulationCount	Computes element-wise population count (a.k.a.
Pow<T extends TType>	Computes the power of one value to another.
PrefetchDataset	Creates a dataset that asynchronously prefetches elements from `input_dataset`.
Prelinearize	An op which linearizes one Tensor value to an opaque variant tensor.
PrelinearizeTuple	An op which linearizes multiple Tensor values to an opaque variant tensor.
PreventGradient<T extends TType>	An identity op that triggers an error if a gradient is requested.
Print	Prints a string scalar.
PriorityQueue	A queue that produces elements sorted by the first component value.
PrivateThreadPoolDataset	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
Prod<T extends TType>	Computes the product of elements across dimensions of a tensor.
Qr<T extends TType>	Computes the QR decompositions of one or more matrices.
Quantize<T extends TType>	Quantize the 'input' tensor of type float to 'output' tensor of type 'T'.
QuantizeAndDequantize<T extends TNumber>	Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV3<T extends TNumber>	Quantizes then dequantizes a tensor.
QuantizeAndDequantizeV4<T extends TNumber>	Returns the gradient of `quantization.QuantizeAndDequantizeV4`.
QuantizeAndDequantizeV4Grad<T extends TNumber>	Returns the gradient of `QuantizeAndDequantizeV4`.
QuantizeDownAndShrinkRange<U extends TType>	Convert the quantized 'input' tensor into a lower-precision 'output', using the actual distribution of the values to maximize the usage of the lower bit depth and adjusting the output min and max ranges accordingly.
QuantizedAdd<V extends TType>	Returns x + y element-wise, working on quantized buffers.
QuantizedAvgPool<T extends TType>	Produces the average pool of the input tensor for quantized types.
QuantizedBatchNormWithGlobalNormalization<U extends TType>	Quantized Batch normalization.
QuantizedBiasAdd<V extends TType>	Adds Tensor 'bias' to Tensor 'input' for Quantized types.
QuantizedConcat<T extends TType>	Concatenates quantized tensors along one dimension.
QuantizedConv2DAndRelu<V extends TType>
QuantizedConv2DAndReluAndRequantize<V extends TType>
QuantizedConv2DAndRequantize<V extends TType>
QuantizedConv2DPerChannel<V extends TType>	Computes QuantizedConv2D per channel.
QuantizedConv2DWithBias<V extends TType>
QuantizedConv2DWithBiasAndRelu<V extends TType>
QuantizedConv2DWithBiasAndReluAndRequantize<W extends TType>
QuantizedConv2DWithBiasAndRequantize<W extends TType>
QuantizedConv2DWithBiasSignedSumAndReluAndRequantize<X extends TType>
QuantizedConv2DWithBiasSumAndRelu<V extends TType>
QuantizedConv2DWithBiasSumAndReluAndRequantize<X extends TType>
QuantizedConv2d<V extends TType>	Computes a 2D convolution given quantized 4D input and filter tensors.
QuantizedDepthwiseConv2D<V extends TType>	Computes quantized depthwise Conv2D.
QuantizedDepthwiseConv2DWithBias<V extends TType>	Computes quantized depthwise Conv2D with Bias.
QuantizedDepthwiseConv2DWithBiasAndRelu<V extends TType>	Computes quantized depthwise Conv2D with Bias and Relu.
QuantizedDepthwiseConv2DWithBiasAndReluAndRequantize<W extends TType>	Computes quantized depthwise Conv2D with Bias, Relu and Requantize.
QuantizedInstanceNorm<T extends TType>	Quantized Instance normalization.
QuantizedMatMul<V extends TType>	Perform a quantized matrix multiplication of `a` by the matrix `b`.
QuantizedMatMulWithBias<W extends TType>	Performs a quantized matrix multiplication of `a` by the matrix `b` with bias add.
QuantizedMatMulWithBiasAndDequantize<W extends TNumber>
QuantizedMatMulWithBiasAndRelu<V extends TType>	Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu fusion.
QuantizedMatMulWithBiasAndReluAndRequantize<W extends TType>	Perform a quantized matrix multiplication of `a` by the matrix `b` with bias add and relu and requantize fusion.
QuantizedMatMulWithBiasAndRequantize<W extends TType>
QuantizedMaxPool<T extends TType>	Produces the max pool of the input tensor for quantized types.
QuantizedMul<V extends TType>	Returns x * y element-wise, working on quantized buffers.
QuantizedRelu<U extends TType>	Computes Quantized Rectified Linear: `max(features, 0)`
QuantizedRelu6<U extends TType>	Computes Quantized Rectified Linear 6: `min(max(features, 0), 6)`
QuantizedReluX<U extends TType>	Computes Quantized Rectified Linear X: `min(max(features, 0), max_value)`
QuantizedReshape<T extends TType>	Reshapes a quantized tensor as per the Reshape op.
QuantizedResizeBilinear<T extends TType>	Resize quantized `images` to `size` using quantized bilinear interpolation.
QueueClose	Closes the given queue.
QueueDequeue	Dequeues a tuple of one or more tensors from the given queue.
QueueDequeueMany	Dequeues `n` tuples of one or more tensors from the given queue.
QueueDequeueUpTo	Dequeues `n` tuples of one or more tensors from the given queue.
QueueEnqueue	Enqueues a tuple of one or more tensors in the given queue.
QueueEnqueueMany	Enqueues zero or more tuples of one or more tensors in the given queue.
QueueIsClosed	Returns true if queue is closed.
QueueSize	Computes the number of elements in the given queue.
RaggedBincount<U extends TNumber>	Counts the number of occurrences of each value in an integer array.
RaggedCountSparseOutput<U extends TNumber>	Performs sparse-output bin counting for a ragged tensor input.
RaggedCross<T extends TType, U extends TNumber>	Generates a feature cross from a list of tensors, and returns it as a RaggedTensor.
RaggedGather<T extends TNumber, U extends TType>	Gather ragged slices from `params` axis `0` according to `indices`.
RaggedRange<U extends TNumber, T extends TNumber>	Returns a `RaggedTensor` containing the specified sequences of numbers.
RaggedTensorFromVariant<U extends TNumber, T extends TType>	Decodes a `variant` Tensor into a `RaggedTensor`.
RaggedTensorToSparse<U extends TType>	Converts a `RaggedTensor` into a `SparseTensor` with the same values.
RaggedTensorToTensor<U extends TType>	Create a dense tensor from a ragged tensor, possibly altering its shape.
RaggedTensorToVariant	Encodes a `RaggedTensor` into a `variant` Tensor.
RaggedTensorToVariantGradient<U extends TType>	Helper used to compute the gradient for `RaggedTensorToVariant`.
RandomCrop<T extends TNumber>	Randomly crop `image`.
RandomDataset	Creates a Dataset that returns pseudorandom numbers.
RandomGamma<U extends TNumber>	Outputs random values from the Gamma distribution(s) described by alpha.
RandomGammaGrad<T extends TNumber>	Computes the derivative of a Gamma random sample w.r.t.
RandomPoisson<V extends TNumber>	Outputs random values from the Poisson distribution(s) described by rate.
RandomShuffle<T extends TType>	Randomly shuffles a tensor along its first dimension.
RandomShuffleQueue	A queue that randomizes the order of elements.
RandomStandardNormal<U extends TNumber>	Outputs random values from a normal distribution.
RandomUniform<U extends TNumber>	Outputs random values from a uniform distribution.
RandomUniformInt<U extends TNumber>	Outputs random integers from a uniform distribution.
Range<T extends TNumber>	Creates a sequence of numbers.
RangeDataset	Creates a dataset with a range of values.
Rank	Returns the rank of a tensor.
RawOp	A base class for `Op` implementations that are backed by a single `Operation`.
ReadFile	Reads and outputs the entire contents of the input filename.
ReadVariableOp<T extends TType>	Reads the value of a variable.
ReaderNumRecordsProduced	Returns the number of records this Reader has produced.
ReaderNumWorkUnitsCompleted	Returns the number of work units this Reader has finished processing.
ReaderRead	Returns the next record (key, value pair) produced by a Reader.
ReaderReadUpTo	Returns up to `num_records` (key, value) pairs produced by a Reader.
ReaderReset	Restore a Reader to its initial clean state.
ReaderRestoreState	Restore a reader to a previously saved state.
ReaderSerializeState	Produce a string tensor that encodes the state of a Reader.
Real<U extends TNumber>	Returns the real part of a complex number.
RealDiv<T extends TType>	Returns x / y element-wise for real types.
RebatchDataset	Creates a dataset that changes the batch size.
RebatchDatasetV2	Creates a dataset that changes the batch size.
Reciprocal<T extends TType>	Computes the reciprocal of x element-wise.
ReciprocalGrad<T extends TType>	Computes the gradient for the inverse of `x` wrt its input.
RecordInput	Emits randomized records.
Recv<T extends TType>	Receives the named tensor from another XLA computation.
RecvTPUEmbeddingActivations	An op that receives embedding activations on the TPU.
Reduce<T extends TNumber>	Mutually reduces multiple tensors of identical type and shape.
ReduceAll	Computes the "logical and" of elements across dimensions of a tensor.
ReduceAny	Computes the "logical or" of elements across dimensions of a tensor.
ReduceJoin	Joins a string Tensor across the given dimensions.
ReduceMax<T extends TType>	Computes the maximum of elements across dimensions of a tensor.
ReduceMin<T extends TType>	Computes the minimum of elements across dimensions of a tensor.
ReduceProd<T extends TType>	Computes the product of elements across dimensions of a tensor.
ReduceSum<T extends TType>	Computes the sum of elements across dimensions of a tensor.
ReduceV2<T extends TNumber>	Mutually reduces multiple tensors of identical type and shape.
RefEnter<T extends TType>	Creates or finds a child frame, and makes `data` available to the child frame.
RefExit<T extends TType>	Exits the current frame to its parent frame.
RefIdentity<T extends TType>	Return the same ref tensor as the input ref tensor.
RefMerge<T extends TType>	Forwards the value of an available tensor from `inputs` to `output`.
RefNextIteration<T extends TType>	Makes its input available to the next iteration.
RefSelect<T extends TType>	Forwards the `index`th element of `inputs` to `output`.
RefSwitch<T extends TType>	Forwards the ref tensor `data` to the output port determined by `pred`.
RegexFullMatch	Check if the input matches the regex pattern.
RegexReplace	Replaces matches of the `pattern` regular expression in `input` with the replacement string provided in `rewrite`.
RegisterDataset	Registers a dataset with the tf.data service.
Relu<T extends TType>	Computes rectified linear: `max(features, 0)`.
Relu6<T extends TNumber>	Computes rectified linear 6: `min(max(features, 0), 6)`.
Relu6Grad<T extends TNumber>	Computes rectified linear 6 gradients for a Relu6 operation.
ReluGrad<T extends TNumber>	Computes rectified linear gradients for a Relu operation.
RemoteFusedGraphExecute	Execute a sub graph on a remote processor.
RepeatDataset	Creates a dataset that emits the outputs of `input_dataset` `count` times.
ReplicaId	Replica ID.
ReplicateMetadata	Metadata indicating how the TPU computation should be replicated.
ReplicatedInput<T extends TType>	Connects N inputs to an N-way replicated TPU computation.
ReplicatedOutput<T extends TType>	Connects N outputs from an N-way replicated TPU computation.
RequantizationRange	Computes a range that covers the actual values present in a quantized tensor.
RequantizationRangePerChannel	Computes requantization range per channel.
Requantize<U extends TType>	Converts the quantized `input` tensor into a lower-precision `output`.
RequantizePerChannel<U extends TType>	Requantizes input with min and max values known per channel.
Reshape<T extends TType>	Reshapes a tensor.
ResizeArea	Resize `images` to `size` using area interpolation.
ResizeBicubic	Resize `images` to `size` using bicubic interpolation.
ResizeBicubicGrad<T extends TNumber>	Computes the gradient of bicubic interpolation.
ResizeBilinear	Resize `images` to `size` using bilinear interpolation.
ResizeBilinearGrad<T extends TNumber>	Computes the gradient of bilinear interpolation.
ResizeNearestNeighbor<T extends TNumber>	Resize `images` to `size` using nearest neighbor interpolation.
ResizeNearestNeighborGrad<T extends TNumber>	Computes the gradient of nearest neighbor interpolation.
ResourceAccumulatorApplyGradient	Applies a gradient to a given accumulator.
ResourceAccumulatorNumAccumulated	Returns the number of gradients aggregated in the given accumulators.
ResourceAccumulatorSetGlobalStep	Updates the accumulator with a new value for global_step.
ResourceAccumulatorTakeGradient<T extends TType>	Extracts the average gradient in the given ConditionalAccumulator.
ResourceApplyAdaMax	Update '*var' according to the AdaMax algorithm.
ResourceApplyAdadelta	Update '*var' according to the adadelta scheme.
ResourceApplyAdagrad	Update '*var' according to the adagrad scheme.
ResourceApplyAdagradDa	Update '*var' according to the proximal adagrad scheme.
ResourceApplyAdam	Update '*var' according to the Adam algorithm.
ResourceApplyAdamWithAmsgrad	Update '*var' according to the Adam algorithm.
ResourceApplyAddSign	Update '*var' according to the AddSign update.
ResourceApplyCenteredRmsProp	Update '*var' according to the centered RMSProp algorithm.
ResourceApplyFtrl	Update '*var' according to the Ftrl-proximal scheme.
ResourceApplyGradientDescent	Update 'var' by subtracting 'alpha' 'delta' from it.
ResourceApplyKerasMomentum	Update '*var' according to the momentum scheme.
ResourceApplyMomentum	Update '*var' according to the momentum scheme.
ResourceApplyPowerSign	Update '*var' according to the AddSign update.
ResourceApplyProximalAdagrad	Update 'var' and 'accum' according to FOBOS with Adagrad learning rate.
ResourceApplyProximalGradientDescent	Update '*var' as FOBOS algorithm with fixed learning rate.
ResourceApplyRmsProp	Update '*var' according to the RMSProp algorithm.
ResourceConditionalAccumulator	A conditional accumulator for aggregating gradients.
ResourceCountUpTo<T extends TNumber>	Increments variable pointed to by 'resource' until it reaches 'limit'.
ResourceGather<U extends TType>	Gather slices from the variable pointed to by `resource` according to `indices`.
ResourceGatherNd<U extends TType>
ResourceScatterAdd	Adds sparse updates to the variable referenced by `resource`.
ResourceScatterDiv	Divides sparse updates into the variable referenced by `resource`.
ResourceScatterMax	Reduces sparse updates into the variable referenced by `resource` using the `max` operation.
ResourceScatterMin	Reduces sparse updates into the variable referenced by `resource` using the `min` operation.
ResourceScatterMul	Multiplies sparse updates into the variable referenced by `resource`.
ResourceScatterNdAdd	Applies sparse addition to individual values or slices in a Variable.
ResourceScatterNdMax
ResourceScatterNdMin
ResourceScatterNdSub	Applies sparse subtraction to individual values or slices in a Variable.
ResourceScatterNdUpdate	Applies sparse `updates` to individual values or slices within a given variable according to `indices`.
ResourceScatterSub	Subtracts sparse updates from the variable referenced by `resource`.
ResourceScatterUpdate	Assigns sparse updates to the variable referenced by `resource`.
ResourceSparseApplyAdadelta	var: Should be from a Variable().
ResourceSparseApplyAdagrad	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
ResourceSparseApplyAdagradDa	Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
ResourceSparseApplyAdagradV2	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
ResourceSparseApplyCenteredRmsProp	Update '*var' according to the centered RMSProp algorithm.
ResourceSparseApplyFtrl	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
ResourceSparseApplyKerasMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
ResourceSparseApplyMomentum	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
ResourceSparseApplyProximalAdagrad	Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
ResourceSparseApplyProximalGradientDescent	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
ResourceSparseApplyRmsProp	Update '*var' according to the RMSProp algorithm.
ResourceStridedSliceAssign	Assign `value` to the sliced l-value reference of `ref`.
Restore	Restores tensors from a V2 checkpoint.
RestoreSlice<T extends TType>	Restores a tensor from checkpoint files.
RetrieveTPUEmbeddingADAMParameters	Retrieve ADAM embedding parameters.
RetrieveTPUEmbeddingADAMParametersGradAccumDebug	Retrieve ADAM embedding parameters with debug support.
RetrieveTPUEmbeddingAdadeltaParameters	Retrieve Adadelta embedding parameters.
RetrieveTPUEmbeddingAdadeltaParametersGradAccumDebug	Retrieve Adadelta embedding parameters with debug support.
RetrieveTPUEmbeddingAdagradParameters	Retrieve Adagrad embedding parameters.
RetrieveTPUEmbeddingAdagradParametersGradAccumDebug	Retrieve Adagrad embedding parameters with debug support.
RetrieveTPUEmbeddingCenteredRMSPropParameters	Retrieve centered RMSProp embedding parameters.
RetrieveTPUEmbeddingFTRLParameters	Retrieve FTRL embedding parameters.
RetrieveTPUEmbeddingFTRLParametersGradAccumDebug	Retrieve FTRL embedding parameters with debug support.
RetrieveTPUEmbeddingMDLAdagradLightParameters	Retrieve MDL Adagrad Light embedding parameters.
RetrieveTPUEmbeddingMomentumParameters	Retrieve Momentum embedding parameters.
RetrieveTPUEmbeddingMomentumParametersGradAccumDebug	Retrieve Momentum embedding parameters with debug support.
RetrieveTPUEmbeddingProximalAdagradParameters	Retrieve proximal Adagrad embedding parameters.
RetrieveTPUEmbeddingProximalAdagradParametersGradAccumDebug	Retrieve proximal Adagrad embedding parameters with debug support.
RetrieveTPUEmbeddingProximalYogiParameters
RetrieveTPUEmbeddingProximalYogiParametersGradAccumDebug
RetrieveTPUEmbeddingRMSPropParameters	Retrieve RMSProp embedding parameters.
RetrieveTPUEmbeddingRMSPropParametersGradAccumDebug	Retrieve RMSProp embedding parameters with debug support.
RetrieveTPUEmbeddingStochasticGradientDescentParameters	Retrieve SGD embedding parameters.
RetrieveTPUEmbeddingStochasticGradientDescentParametersGradAccumDebug	Retrieve SGD embedding parameters with debug support.
Reverse<T extends TType>	Reverses specific dimensions of a tensor.
ReverseSequence<T extends TType>	Reverses variable length slices.
Rfft<U extends TType>	Real-valued fast Fourier transform.
Rfft2d<U extends TType>	2D real-valued fast Fourier transform.
Rfft3d<U extends TType>	3D real-valued fast Fourier transform.
RgbToHsv<T extends TNumber>	Converts one or more images from RGB to HSV.
RightShift<T extends TNumber>	Elementwise computes the bitwise right-shift of `x` and `y`.
Rint<T extends TNumber>	Returns element-wise integer closest to x.
RngReadAndSkip	Advance the counter of a counter-based RNG.
RngSkip	Advance the counter of a counter-based RNG.
Roll<T extends TType>	Rolls the elements of a tensor along an axis.
Round<T extends TType>	Rounds the values of a tensor to the nearest integer, element-wise.
Rpc	Perform batches of RPC requests.
Rsqrt<T extends TType>	Computes reciprocal of square root of x element-wise.
RsqrtGrad<T extends TType>	Computes the gradient for the rsqrt of `x` wrt its input.
SampleDistortedBoundingBox<T extends TNumber>	Generate a single randomly distorted bounding box for an image.
SamplingDataset	Creates a dataset that takes a Bernoulli sample of the contents of another dataset.
Save	Saves tensors in V2 checkpoint format.
SaveSlices	Saves input tensors slices to disk.
ScalarSummary	Outputs a `Summary` protocol buffer with scalar values.
ScaleAndTranslate
ScaleAndTranslateGrad<T extends TNumber>
ScatterAdd<T extends TType>	Adds sparse updates to a variable reference.
ScatterDiv<T extends TType>	Divides a variable reference by sparse updates.
ScatterMax<T extends TNumber>	Reduces sparse updates into a variable reference using the `max` operation.
ScatterMin<T extends TNumber>	Reduces sparse updates into a variable reference using the `min` operation.
ScatterMul<T extends TType>	Multiplies sparse updates into a variable reference.
ScatterNd<U extends TType>	Scatter `updates` into a new tensor according to `indices`.
ScatterNdAdd<T extends TType>	Applies sparse addition to individual values or slices in a Variable.
ScatterNdMax<T extends TType>	Computes element-wise maximum.
ScatterNdMin<T extends TType>	Computes element-wise minimum.
ScatterNdNonAliasingAdd<T extends TType>	Applies sparse addition to `input` using individual values or slices from `updates` according to indices `indices`.
ScatterNdSub<T extends TType>	Applies sparse subtraction to individual values or slices in a Variable.
ScatterNdUpdate<T extends TType>	Applies sparse `updates` to individual values or slices within a given variable according to `indices`.
ScatterSub<T extends TType>	Subtracts sparse updates to a variable reference.
ScatterUpdate<T extends TType>	Applies sparse updates to a variable reference.
SdcaFprint	Computes fingerprints of the input strings.
SdcaOptimizer	Distributed version of Stochastic Dual Coordinate Ascent (SDCA) optimizer for linear models with L1 + L2 regularization.
SdcaShrinkL1	Applies L1 regularization shrink step on the parameters.
SegmentMax<T extends TNumber>	Computes the maximum along segments of a tensor.
SegmentMean<T extends TType>	Computes the mean along segments of a tensor.
SegmentMin<T extends TNumber>	Computes the minimum along segments of a tensor.
SegmentProd<T extends TType>	Computes the product along segments of a tensor.
SegmentSum<T extends TType>	Computes the sum along segments of a tensor.
Select<T extends TType>
SelfAdjointEig<T extends TType>	Computes the eigen decomposition of a batch of self-adjoint matrices (Note: Only real inputs are supported).
Selu<T extends TNumber>	Computes scaled exponential linear: `scale * alpha * (exp(features) - 1)` if < 0, `scale * features` otherwise.
SeluGrad<T extends TNumber>	Computes gradients for the scaled exponential linear (Selu) operation.
Send	Sends the named tensor to another XLA computation.
SendTPUEmbeddingGradients	Performs gradient updates of embedding tables.
SerializeIterator	Converts the given `resource_handle` representing an iterator to a variant tensor.
SerializeManySparse<U extends TType>	Serialize an `N`-minibatch `SparseTensor` into an `[N, 3]` `Tensor` object.
SerializeSparse<U extends TType>	Serialize a `SparseTensor` into a `[3]` `Tensor` object.
SerializeTensor	Transforms a Tensor into a serialized TensorProto proto.
SetDiff1d<T extends TType, U extends TNumber>	Computes the difference between two lists of numbers or strings.
SetSize	Number of unique elements along last dimension of input `set`.
SetStatsAggregatorDataset
Shape<U extends TNumber>	Returns the shape of a tensor.
ShapeN<U extends TNumber>	Returns shape of tensors.
ShardDataset	Creates a `Dataset` that includes only 1/`num_shards` of this dataset.
ShardedFilename	Generate a sharded filename.
ShardedFilespec	Generate a glob pattern matching all sharded file names.
Sharding<T extends TType>	An op which shards the input based on the given sharding attribute.
ShuffleAndRepeatDataset
ShuffleDataset
ShutdownDistributedTPU	Shuts down a running distributed TPU system.
Sigmoid<T extends TType>	Computes sigmoid of `x` element-wise.
SigmoidGrad<T extends TType>	Computes the gradient of the sigmoid of `x` wrt its input.
Sign<T extends TType>	Returns an element-wise indication of the sign of a number.
Sin<T extends TType>	Computes sine of x element-wise.
Sinh<T extends TType>	Computes hyperbolic sine of x element-wise.
Size<U extends TNumber>	Returns the size of a tensor.
SkipDataset	Creates a dataset that skips `count` elements from the `input_dataset`.
Skipgram	Parses a text file and creates a batch of examples.
SleepDataset
Slice<T extends TType>	Return a slice from 'input'.
SlidingWindowDataset	Creates a dataset that passes a sliding window over `input_dataset`.
Snapshot<T extends TType>	Returns a copy of the input tensor.
SobolSample<T extends TNumber>	Generates points from the Sobol sequence.
Softmax<T extends TNumber>	Computes softmax activations.
SoftmaxCrossEntropyWithLogits<T extends TNumber>	Computes softmax cross entropy cost and gradients to backpropagate.
Softplus<T extends TNumber>	Computes softplus: `log(exp(features) + 1)`.
SoftplusGrad<T extends TNumber>	Computes softplus gradients for a softplus operation.
Softsign<T extends TNumber>	Computes softsign: `features / (abs(features) + 1)`.
SoftsignGrad<T extends TNumber>	Computes softsign gradients for a softsign operation.
Solve<T extends TType>	Solves systems of linear equations.
Sort<T extends TType>	Wraps the XLA Sort operator, documented at https://www.tensorflow.org/performance/xla/operation_semantics#sort .
SpaceToBatch<T extends TType>	SpaceToBatch for 4-D tensors of type T.
SpaceToBatchNd<T extends TType>	SpaceToBatch for N-D tensors of type T.
SpaceToDepth<T extends TType>	SpaceToDepth for tensors of type T.
SparseAccumulatorApplyGradient	Applies a sparse gradient to a given accumulator.
SparseAccumulatorTakeGradient<T extends TType>	Extracts the average sparse gradient in a SparseConditionalAccumulator.
SparseAdd<T extends TType>	Adds two `SparseTensor` objects to produce another `SparseTensor`.
SparseAddGrad<T extends TType>	The gradient operator for the SparseAdd op.
SparseApplyAdadelta<T extends TType>	var: Should be from a Variable().
SparseApplyAdagrad<T extends TType>	Update relevant entries in 'var' and 'accum' according to the adagrad scheme.
SparseApplyAdagradDa<T extends TType>	Update entries in 'var' and 'accum' according to the proximal adagrad scheme.
SparseApplyCenteredRmsProp<T extends TType>	Update '*var' according to the centered RMSProp algorithm.
SparseApplyFtrl<T extends TType>	Update relevant entries in '*var' according to the Ftrl-proximal scheme.
SparseApplyMomentum<T extends TType>	Update relevant entries in 'var' and 'accum' according to the momentum scheme.
SparseApplyProximalAdagrad<T extends TType>	Sparse update entries in 'var' and 'accum' according to FOBOS algorithm.
SparseApplyProximalGradientDescent<T extends TType>	Sparse update '*var' as FOBOS algorithm with fixed learning rate.
SparseApplyRmsProp<T extends TType>	Update '*var' according to the RMSProp algorithm.
SparseBincount<U extends TNumber>	Counts the number of occurrences of each value in an integer array.
SparseConcat<T extends TType>	Concatenates a list of `SparseTensor` along the specified dimension.
SparseConditionalAccumulator	A conditional accumulator for aggregating sparse gradients.
SparseCountSparseOutput<U extends TNumber>	Performs sparse-output bin counting for a sparse tensor input.
SparseCross	Generates sparse cross from a list of sparse and dense tensors.
SparseCrossHashed	Generates sparse cross from a list of sparse and dense tensors.
SparseDenseCwiseAdd<T extends TType>	Adds up a SparseTensor and a dense Tensor, using these special rules: (1) Broadcasts the dense side to have the same shape as the sparse side, if eligible; (2) Then, only the dense values pointed to by the indices of the SparseTensor participate in the cwise addition.
SparseDenseCwiseDiv<T extends TType>	Component-wise divides a SparseTensor by a dense Tensor.
SparseDenseCwiseMul<T extends TType>	Component-wise multiplies a SparseTensor by a dense Tensor.
SparseFillEmptyRows<T extends TType>	Fills empty rows in the input 2-D `SparseTensor` with a default value.
SparseFillEmptyRowsGrad<T extends TType>	The gradient of SparseFillEmptyRows.
SparseMatMul	Multiply matrix "a" by matrix "b".
SparseMatrixAdd	Sparse addition of two CSR matrices, C = alpha * A + beta * B.
SparseMatrixMatMul<T extends TType>	Matrix-multiplies a sparse matrix with a dense matrix.
SparseMatrixMul	Element-wise multiplication of a sparse matrix with a dense tensor.
SparseMatrixNNZ	Returns the number of nonzeroes of `sparse_matrix`.
SparseMatrixOrderingAMD	Computes the Approximate Minimum Degree (AMD) ordering of `input`.
SparseMatrixSoftmax	Calculates the softmax of a CSRSparseMatrix.
SparseMatrixSoftmaxGrad	Calculates the gradient of the SparseMatrixSoftmax op.
SparseMatrixSparseCholesky	Computes the sparse Cholesky decomposition of `input`.
SparseMatrixSparseMatMul	Sparse-matrix-multiplies two CSR matrices `a` and `b`.
SparseMatrixTranspose	Transposes the inner (matrix) dimensions of a CSRSparseMatrix.
SparseMatrixZeros	Creates an all-zeros CSRSparseMatrix with shape `dense_shape`.
SparseReduceMax<T extends TNumber>	Computes the max of elements across dimensions of a SparseTensor.
SparseReduceMaxSparse<T extends TNumber>	Computes the max of elements across dimensions of a SparseTensor.
SparseReduceSum<T extends TType>	Computes the sum of elements across dimensions of a SparseTensor.
SparseReduceSumSparse<T extends TType>	Computes the sum of elements across dimensions of a SparseTensor.
SparseReorder<T extends TType>	Reorders a SparseTensor into the canonical, row-major ordering.
SparseReshape	Reshapes a SparseTensor to represent values in a new dense shape.
SparseSegmentMean<T extends TNumber>	Computes the mean along sparse segments of a tensor.
SparseSegmentMeanGrad<T extends TNumber>	Computes gradients for SparseSegmentMean.
SparseSegmentMeanWithNumSegments<T extends TNumber>	Computes the mean along sparse segments of a tensor.
SparseSegmentSqrtN<T extends TNumber>	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSqrtNGrad<T extends TNumber>	Computes gradients for SparseSegmentSqrtN.
SparseSegmentSqrtNWithNumSegments<T extends TNumber>	Computes the sum along sparse segments of a tensor divided by the sqrt of N.
SparseSegmentSum<T extends TNumber>	Computes the sum along sparse segments of a tensor.
SparseSegmentSumWithNumSegments<T extends TNumber>	Computes the sum along sparse segments of a tensor.
SparseSlice<T extends TType>	Slice a `SparseTensor` based on the `start` and `size`.
SparseSliceGrad<T extends TType>	The gradient operator for the SparseSlice op.
SparseSoftmax<T extends TNumber>	Applies softmax to a batched N-D `SparseTensor`.
SparseSoftmaxCrossEntropyWithLogits<T extends TNumber>	Computes softmax cross entropy cost and gradients to backpropagate.
SparseSparseMaximum<T extends TNumber>	Returns the element-wise max of two SparseTensors.
SparseSparseMinimum<T extends TType>	Returns the element-wise min of two SparseTensors.
SparseSplit<T extends TType>	Split a `SparseTensor` into `num_split` tensors along one dimension.
SparseTensorDenseAdd<U extends TType>	Adds up a `SparseTensor` and a dense `Tensor`, producing a dense `Tensor`.
SparseTensorDenseMatMul<U extends TType>	Multiply SparseTensor (of rank 2) "A" by dense matrix "B".
SparseTensorSliceDataset	Creates a dataset that splits a SparseTensor into elements row-wise.
SparseTensorToCSRSparseMatrix	Converts a SparseTensor to a (possibly batched) CSRSparseMatrix.
SparseToDense<U extends TType>	Converts a sparse representation into a dense tensor.
SparseToSparseSetOperation<T extends TType>	Applies set operation along last dimension of 2 `SparseTensor` inputs.
Spence<T extends TNumber>
Split<T extends TType>	Splits a tensor into `num_split` tensors along one dimension.
SplitV<T extends TType>	Splits a tensor into `num_split` tensors along one dimension.
SqlDataset	Creates a dataset that executes a SQL query and emits rows of the result set.
Sqrt<T extends TType>	Computes square root of x element-wise.
SqrtGrad<T extends TType>	Computes the gradient for the sqrt of `x` wrt its input.
Sqrtm<T extends TType>	Computes the matrix square root of one or more square matrices: matmul(sqrtm(A), sqrtm(A)) = A The input matrix should be invertible.
Square<T extends TType>	Computes square of x element-wise.
SquaredDifference<T extends TType>	Returns conj(x - y)(x - y) element-wise.
Squeeze<T extends TType>	Removes dimensions of size 1 from the shape of a tensor.
Stack<T extends TType>	Packs a list of `N` rank-`R` tensors into one rank-`(R+1)` tensor.
Stage	Stage values similar to a lightweight Enqueue.
StageClear	Op removes all elements in the underlying container.
StagePeek	Op peeks at the values at the specified index.
StageSize	Op returns the number of elements in the underlying container.
StatefulRandomBinomial<V extends TNumber>
StatefulStandardNormal<U extends TType>	Outputs random values from a normal distribution.
StatefulTruncatedNormal<U extends TType>	Outputs random values from a truncated normal distribution.
StatefulUniform<U extends TType>	Outputs random values from a uniform distribution.
StatefulUniformFullInt<U extends TType>	Outputs random integers from a uniform distribution.
StatefulUniformInt<U extends TType>	Outputs random integers from a uniform distribution.
StatelessMultinomial<V extends TNumber>	Draws samples from a multinomial distribution.
StatelessParameterizedTruncatedNormal<V extends TNumber>
StatelessRandomBinomial<W extends TNumber>	Outputs deterministic pseudorandom random numbers from a binomial distribution.
StatelessRandomGamma<V extends TNumber>	Outputs deterministic pseudorandom random numbers from a gamma distribution.
StatelessRandomGetKeyCounterAlg	Picks the best algorithm based on device, and scrambles seed into key and counter.
StatelessRandomNormal<V extends TNumber>	Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomNormalV2<U extends TNumber>	Outputs deterministic pseudorandom values from a normal distribution.
StatelessRandomPoisson<W extends TNumber>	Outputs deterministic pseudorandom random numbers from a Poisson distribution.
StatelessRandomUniform<V extends TNumber>	Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessRandomUniformFullInt<V extends TNumber>	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformFullIntV2<U extends TNumber>	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformInt<V extends TNumber>	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformIntV2<U extends TNumber>	Outputs deterministic pseudorandom random integers from a uniform distribution.
StatelessRandomUniformV2<U extends TNumber>	Outputs deterministic pseudorandom random values from a uniform distribution.
StatelessSampleDistortedBoundingBox<T extends TNumber>	Generate a randomly distorted bounding box for an image deterministically.
StatelessTruncatedNormal<V extends TNumber>	Outputs deterministic pseudorandom values from a truncated normal distribution.
StatelessTruncatedNormalV2<U extends TNumber>	Outputs deterministic pseudorandom values from a truncated normal distribution.
StaticRegexFullMatch	Check if the input matches the regex pattern.
StaticRegexReplace	Replaces the match of pattern in input with rewrite.
StatsAggregatorHandle
StatsAggregatorSetSummaryWriter	Set a summary_writer_interface to record statistics using given stats_aggregator.
StatsAggregatorSummary	Produces a summary of any statistics recorded by the given statistics manager.
StopGradient<T extends TType>	Stops gradient computation.
StridedSlice<T extends TType>	Return a strided slice from `input`.
StridedSliceAssign<T extends TType>	Assign `value` to the sliced l-value reference of `ref`.
StridedSliceGrad<U extends TType>	Returns the gradient of `StridedSlice`.
StringFormat	Formats a string template using a list of tensors.
StringLength	String lengths of `input`.
StringNGrams<T extends TNumber>	Creates ngrams from ragged string data.
StringSplit	Split elements of `source` based on `sep` into a `SparseTensor`.
Strip	Strip leading and trailing whitespaces from the Tensor.
Sub<T extends TType>	Returns x - y element-wise.
Substr	Return substrings from `Tensor` of strings.
Sum<T extends TType>	Computes the sum of elements across dimensions of a tensor.
SummaryWriter
Svd<T extends TType>	Computes the eigen decomposition of a batch of self-adjoint matrices (Note: Only real inputs are supported).
SwitchCond<T extends TType>	Forwards `data` to the output port determined by `pred`.
TPUCompilationResult	Returns the result of a TPU compilation.
TPUEmbeddingActivations	An op enabling differentiation of TPU Embeddings.
TPUReplicateMetadata	Metadata indicating how the TPU computation should be replicated.
TPUReplicatedInput<T extends TType>	Connects N inputs to an N-way replicated TPU computation.
TPUReplicatedOutput<T extends TType>	Connects N outputs from an N-way replicated TPU computation.
TakeDataset	Creates a dataset that contains `count` elements from the `input_dataset`.
TakeManySparseFromTensorsMap<T extends TType>	Read `SparseTensors` from a `SparseTensorsMap` and concatenate them.
Tan<T extends TType>	Computes tan of x element-wise.
Tanh<T extends TType>	Computes hyperbolic tangent of `x` element-wise.
TanhGrad<T extends TType>	Computes the gradient for the tanh of `x` wrt its input.
TemporaryVariable<T extends TType>	Returns a tensor that may be mutated, but only persists within a single step.
TensorArray	An array of Tensors of given size.
TensorArrayClose	Delete the TensorArray from its resource container.
TensorArrayConcat<T extends TType>	Concat the elements from the TensorArray into value `value`.
TensorArrayGather<T extends TType>	Gather specific elements from the TensorArray into output `value`.
TensorArrayGrad	Creates a TensorArray for storing the gradients of values in the given handle.
TensorArrayGradWithShape	Creates a TensorArray for storing multiple gradients of values in the given handle.
TensorArrayPack<T extends TType>
TensorArrayRead<T extends TType>	Read an element from the TensorArray into output `value`.
TensorArrayScatter	Scatter the data from the input value into specific TensorArray elements.
TensorArraySize	Get the current size of the TensorArray.
TensorArraySplit	Split the data from the input value into TensorArray elements.
TensorArrayUnpack
TensorArrayWrite	Push an element onto the tensor_array.
TensorDataset	Creates a dataset that emits `components` as a tuple of tensors once.
TensorDiag<T extends TType>	Returns a diagonal tensor with a given diagonal values.
TensorDiagPart<T extends TType>	Returns the diagonal part of the tensor.
TensorForestCreateTreeVariable	Creates a tree resource and returns a handle to it.
TensorForestTreeDeserialize	Deserializes a proto into the tree handle
TensorForestTreeIsInitializedOp	Checks whether a tree has been initialized.
TensorForestTreePredict	Output the logits for the given input data
TensorForestTreeResourceHandleOp	Creates a handle to a TensorForestTreeResource
TensorForestTreeSerialize	Serializes the tree handle to a proto
TensorForestTreeSize	Get the number of nodes in a tree
TensorListConcat<U extends TType>	Concats all tensors in the list along the 0th dimension.
TensorListConcatLists
TensorListElementShape<T extends TNumber>	The shape of the elements of the given list, as a tensor.
TensorListFromTensor	Creates a TensorList which, when stacked, has the value of `tensor`.
TensorListGather<T extends TType>	Creates a Tensor by indexing into the TensorList.
TensorListGetItem<T extends TType>
TensorListLength	Returns the number of tensors in the input tensor list.
TensorListPopBack<T extends TType>	Returns the last element of the input list as well as a list with all but that element.
TensorListPushBack	Returns a list which has the passed-in `Tensor` as last element and the other elements of the given list in `input_handle`.
TensorListPushBackBatch
TensorListReserve	List of the given size with empty elements.
TensorListResize	Resizes the list.
TensorListScatter	Creates a TensorList by indexing into a Tensor.
TensorListScatterIntoExistingList	Scatters tensor at indices in an input list.
TensorListSetItem
TensorListSplit	Splits a tensor into a list.
TensorListStack<T extends TType>	Stacks all tensors in the list.
TensorMapErase	Returns a tensor map with item from given key erased.
TensorMapHasKey	Returns whether the given key exists in the map.
TensorMapInsert	Returns a map that is the 'input_handle' with the given key-value pair inserted.
TensorMapLookup<U extends TType>	Returns the value from a given key in a tensor map.
TensorMapSize	Returns the number of tensors in the input tensor map.
TensorMapStackKeys<T extends TType>	Returns a Tensor stack of all keys in a tensor map.
TensorScatterNdAdd<T extends TType>	Adds sparse `updates` to an existing tensor according to `indices`.
TensorScatterNdMax<T extends TType>
TensorScatterNdMin<T extends TType>
TensorScatterNdSub<T extends TType>	Subtracts sparse `updates` from an existing tensor according to `indices`.
TensorScatterNdUpdate<T extends TType>	Scatter `updates` into an existing tensor according to `indices`.
TensorSliceDataset	Creates a dataset that emits each dim-0 slice of `components` once.
TensorStridedSliceUpdate<T extends TType>	Assign `value` to the sliced l-value reference of `input`.
TensorSummary	Outputs a `Summary` protocol buffer with a tensor and per-plugin data.
TextLineDataset	Creates a dataset that emits the lines of one or more text files.
TextLineReader	A Reader that outputs the lines of a file delimited by '\n'.
TfRecordDataset	Creates a dataset that emits the records from one or more TFRecord files.
TfRecordReader	A Reader that outputs the records from a TensorFlow Records file.
ThreadPoolDataset	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
ThreadPoolHandle	Creates a dataset that uses a custom thread pool to compute `input_dataset`.
Tile<T extends TType>	Constructs a tensor by tiling a given tensor.
TileGrad<T extends TType>	Returns the gradient of `Tile`.
Timestamp	Provides the time since epoch in seconds.
ToBool	Converts a tensor to a scalar predicate.
ToHashBucket	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToHashBucketFast	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToHashBucketStrong	Converts each string in the input Tensor to its hash mod by a number of buckets.
ToNumber<T extends TNumber>	Converts each string in the input Tensor to the specified numeric type.
TopK<T extends TNumber>	Finds values and indices of the `k` largest elements for the last dimension.
TopKUnique	Returns the TopK unique values in the array in sorted order.
TopKWithUnique	Returns the TopK values in the array in sorted order.
Transpose<T extends TType>	Shuffle dimensions of x according to a permutation.
TriangularSolve<T extends TType>	Solves systems of linear equations with upper or lower triangular matrices by backsubstitution.
TridiagonalMatMul<T extends TType>	Calculate product with tridiagonal matrix.
TridiagonalSolve<T extends TType>	Solves tridiagonal systems of equations.
TruncateDiv<T extends TType>	Returns x / y element-wise for integer types.
TruncateMod<T extends TNumber>	Returns element-wise remainder of division.
TruncatedNormal<U extends TNumber>	Outputs random values from a truncated normal distribution.
TryRpc	Perform batches of RPC requests.
Unbatch<T extends TType>	Reverses the operation of Batch for a single output Tensor.
UnbatchDataset	A dataset that splits the elements of its input into multiple elements.
UnbatchGrad<T extends TType>	Gradient of Unbatch.
UncompressElement	Uncompresses a compressed dataset element.
UnicodeDecode<T extends TNumber>	Decodes each string in `input` into a sequence of Unicode code points.
UnicodeDecodeWithOffsets<T extends TNumber>	Decodes each string in `input` into a sequence of Unicode code points.
UnicodeEncode	Encode a tensor of ints into unicode strings.
UnicodeScript	Determine the script codes of a given tensor of Unicode integer code points.
UnicodeTranscode	Transcode the input text from a source encoding to a destination encoding.
UniformCandidateSampler	Generates labels for candidate sampling with a uniform distribution.
Unique<T extends TType, V extends TNumber>	Finds unique elements along an axis of a tensor.
UniqueDataset	Creates a dataset that contains the unique elements of `input_dataset`.
UniqueWithCounts<T extends TType, V extends TNumber>	Finds unique elements along an axis of a tensor.
UnravelIndex<T extends TNumber>	Converts an array of flat indices into a tuple of coordinate arrays.
UnsortedSegmentJoin	Joins the elements of `inputs` based on `segment_ids`.
UnsortedSegmentMax<T extends TNumber>	Computes the maximum along segments of a tensor.
UnsortedSegmentMin<T extends TNumber>	Computes the minimum along segments of a tensor.
UnsortedSegmentProd<T extends TType>	Computes the product along segments of a tensor.
UnsortedSegmentSum<T extends TType>	Computes the sum along segments of a tensor.
Unstack<T extends TType>	Unpacks a given dimension of a rank-`R` tensor into `num` rank-`(R-1)` tensors.
Unstage	Op is similar to a lightweight Dequeue.
UnwrapDatasetVariant
Upper	Converts all lowercase characters into their respective uppercase replacements.
UpperBound<U extends TNumber>	Applies upper_bound(sorted_search_values, values) along each row.
VarHandleOp	Creates a handle to a Variable resource.
VarIsInitializedOp	Checks whether a resource handle-based variable has been initialized.
Variable<T extends TType>	Holds state in the form of a tensor that persists across steps.
VariableShape<T extends TNumber>	Returns the shape of the variable pointed to by `resource`.
Where	Returns locations of nonzero / true values in a tensor.
WholeFileReader	A Reader that outputs the entire contents of a file as a value.
WindowDataset	Combines (nests of) input elements into a dataset of (nests of) windows.
WorkerHeartbeat	Worker heartbeat op.
WrapDatasetVariant
WriteAudioSummary	Writes an audio summary.
WriteFile	Writes contents to the file at input filename.
WriteGraphSummary	Writes a graph summary.
WriteHistogramSummary	Writes a histogram summary.
WriteImageSummary	Writes an image summary.
WriteRawProtoSummary	Writes a serialized proto summary.
WriteScalarSummary	Writes a scalar summary.
WriteSummary	Writes a tensor summary.
Xdivy<T extends TType>	Returns 0 if x == 0, and x / y otherwise, elementwise.
XlaRecvFromHost<T extends TType>	An op to receive a tensor from the host.
XlaSendToHost	An op to send a tensor to the host.
XlaSetBound	Set a bound for the given input value as a hint to Xla compiler, returns the same value.
XlaSpmdFullToShardShape<T extends TType>	An op used by XLA SPMD partitioner to switch from automatic partitioning to manual partitioning.
XlaSpmdShardToFullShape<T extends TType>	An op used by XLA SPMD partitioner to switch from manual partitioning to automatic partitioning.
Xlog1py<T extends TType>	Returns 0 if x == 0, and x * log1p(y) otherwise, elementwise.
Xlogy<T extends TType>	Returns 0 if x == 0, and x * log(y) otherwise, elementwise.
Zeros<T extends TType>	An operator creating a constant initialized with zeros of the shape given by `dims`.
ZerosLike<T extends TType>	Returns a tensor of zeros with the same shape and type as x.
Zeta<T extends TNumber>	Compute the Hurwitz zeta function \\(\zeta(x, q)\\).
ZipDataset	Creates a dataset that zips together `input_datasets`.
erfinv<T extends TNumber>

A logical unit of computation.

Op implementations provide a strongly typed API for building and executing operations without the use of literals and indexes, as required in internal classes like Operation.

Ops can be classified under two categories:

Raw ops target a single TensorFlow operation and are, in most cases, generated automatically from the OpDef proto definitions exposed by TensorFlow runtime library.
Composite ops execute a series of other ops to accomplish a task logically presented as a single unit of computation.

Public Methods

abstract ExecutionEnvironment	env() Return the execution environment this op was created in.
abstract Operation	op() Return this unit of computation as a single `Operation`.

Public Methods

public abstract ExecutionEnvironment env ()

Return the execution environment this op was created in.

public abstract Operation op ()

Return this unit of computation as a single Operation.

For a raw op, the returned value correspond to the TensorFlow operation wrapped by this op.

For a composite op, the returned value usually correspond to the last operation invoked in a chain or to a NoOp grouping one or more operations as a single unit of computation.

Returns

an Operation