The operation was aborted, typically due to a concurrency issue like sequencer check failures, transaction aborts, etc.

Some entity that we attempted to create (e.g., file or directory) already exists.

The operation was cancelled (typically by the caller).

Unrecoverable data loss or corruption.

Deadline expired before operation could complete.

An extra enum entry to prevent people from writing code that fails to compile when a new code is added.

Operation was rejected because the system is not in a state required for the operation's execution.

Internal errors.

Client specified an invalid argument.

Some requested entity (e.g., file or directory) was not found.

Not an error; returned on success.

Operation tried to iterate past the valid input range.

The caller does not have permission to execute the specified operation.

Some resource has been exhausted, perhaps a per-user quota, or perhaps the entire file system is out of space.

The request does not have valid authentication credentials for the operation.

The service is currently unavailable.

Operation is not implemented or not supported/enabled in this service.

Unknown error.

Disabling the MLIR bridge disables it for all graphs in this session.

Enabling the MLIR bridge enables it for all graphs in this session.

Enable the MLIR bridge on a per graph basis based on an analysis of the features used in the graph.

Enable the MLIR bridge in a fallback mode on a per graph basis based on an analysis of the features used in the graph.

If this field is left unspecified, the MLIR bridge may be selectively enabled on a per graph basis.

Default setting ("off" now, but later expected to be "on").

No optimizations.

L1 is the default level.

The following settings turn on compilation, with higher values being more aggressive.

Enable some aggressive optimizations that use assumptions that TF graphs may break.

The default setting (SCHEDULING and SWAPPING HEURISTICS only).

Use any combination of swapping and recomputation heuristics.

Driven by manual op-level annotations.

Disabled in the meta-optimizer.

Recomputation heuristics will recompute ops (such as Relu activation) during backprop instead of storing them, reducing peak memory usage.

Scheduling will split big ops such as AddN and try to enforce a schedule of the new computations that decreases peak memory usage.

Swapping heuristic will move a tensor from the GPU to the CPU and move it back when needed to reduce peak memory usage.

Internal legacy format.

Deprecated format: tf.Saver() which works with tensorflow::table::Table.

Current format: more efficient.

tf.data.DatasetSpec.

IteratorSpec from data/ops/iterator_ops.py.

Subclasses of tf.ExtensionType.

tf.IndexedSlicesSpec.

tf.OptionalSpec.

PerReplicaSpec from distribute/values.py.

tf.RaggedTensorSpec.

The type registered as type_spec_class_name.

RowPartitionSpec from ragged/row_partition.py.

tf.SparseTensorSpec.

tf.TensorArraySpec.

tf.VariableSpec.

Enum value maps for Code.

Enum value maps for Code.

Enum value maps for ConfigProto_Experimental_MlirBridgeRollout.

Enum value maps for ConfigProto_Experimental_MlirBridgeRollout.

Enum value maps for FunctionSpec_JitCompile.

Enum value maps for FunctionSpec_JitCompile.

Enum value maps for OptimizerOptions_GlobalJitLevel.

Enum value maps for OptimizerOptions_GlobalJitLevel.

Enum value maps for OptimizerOptions_Level.

Enum value maps for OptimizerOptions_Level.

Enum value maps for RewriterConfig_CpuLayout.

Enum value maps for RewriterConfig_CpuLayout.

Enum value maps for RewriterConfig_MemOptType.

Enum value maps for RewriterConfig_MemOptType.

Enum value maps for RewriterConfig_NumIterationsType.

Enum value maps for RewriterConfig_NumIterationsType.

Enum value maps for RewriterConfig_Toggle.

Enum value maps for RewriterConfig_Toggle.

Enum value maps for RunOptions_TraceLevel.

Enum value maps for RunOptions_TraceLevel.

Enum value maps for SaverDef_CheckpointFormatVersion.

Enum value maps for SaverDef_CheckpointFormatVersion.

Enum value maps for TypeSpecProto_TypeSpecClass.

Enum value maps for TypeSpecProto_TypeSpecClass.

Enum value maps for VerifierConfig_Toggle.

Enum value maps for VerifierConfig_Toggle.

An asset file def for a single file or a set of sharded files with the same name.

A protobuf to represent tf.BoundedTensorSpec.

Defines a subgraph in another `GraphDef` as a set of feed points and nodes to be fetched or executed.

Defines a TensorFlow cluster as a set of jobs.

CollectionDef should cover most collections.

AnyList is used for collecting Any protos.

BytesList is used for collecting strings and serialized protobufs.

FloatList is used for collecting float values.

Int64List is used for collecting int, int64 and long values.

NodeList is used for collecting nodes in graph.

Session configuration parameters.

Everything inside Experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat.

Options for initializing DebuggerState in TensorFlow Debugger (tfdbg).

Option for watching a node in TensorFlow Debugger (tfdbg).

Represents a Python dict keyed by `str`.

Represents `FunctionSpec` used in `Function`.

Configuration for breaking down a visible GPU into multiple "virtual" devices.

Defines a single job in a TensorFlow cluster.

Represents a Python list.

NOTE: This protocol buffer is evolving, and will go through revisions in the coming months.

Meta information regarding the graph to be exported.

A pair of tensor name and tensor values.

Represents Python's namedtuple.

Represents None.

Options passed to the graph optimizer.

Represents a (key, value) pair.

Message to describe custom graph optimizer and its parameters.

Metadata output (i.e., non-Tensor) for a single Run() call.

Options for a single Run() call.

Everything inside Experimental is subject to change and is not subject to API stability guarantees in https://www.tensorflow.org/guide/version_compat.

Options for run handler thread pool.

A SavedAsset points to an asset in the MetaGraph.

Stores low-level information about a concrete function.

A function with multiple signatures, possibly with non-Tensor arguments.

A SavedResource represents a TF object that holds state during its lifetime.

A SavedUserObject is an object (in the object-oriented language of the TensorFlow program) of some user- or framework-defined class other than those handled specifically by the other kinds of SavedObjects.

Represents a Variable that is initialized by loading the contents from the checkpoint.

Protocol buffer representing the configuration of a Saver.

Metadata about the session.

SignatureDef defines the signature of a computation supported by a TensorFlow graph.

`StructuredValue` represents a dynamically typed value representing various data structures that are inspired by Python data structures typically used in TensorFlow functions as inputs and outputs.

Defines a connection between two tensors in a `GraphDef`.

Information about a Tensor necessary for feeding or retrieval.

Generic encoding for composite tensors.

For sparse tensors, The COO encoding stores a triple of values, indices, and shape.

A protobuf to represent tf.TensorSpec.

Represents a Python tuple.

Represents a tf.TypeSpec.

The config for graph verifiers.

The canonical error codes for TensorFlow APIs.

An enum that describes the state of the MLIR bridge rollout.

Whether the function should be compiled by XLA.

Control the use of the compiler/jit.

Optimization level.

Enum for layout conversion between NCHW and NHWC on CPU.

Enum controlling the number of times to run optimizers.

TODO(pbar) Turn this into a TraceOptions proto which allows tracing to be controlled in a more orthogonal manner?.

A version number that identifies a different on-disk checkpoint format.