RewriterConfigOrBuilder

interface publique RewriterConfigOrBuilder

Sous-classes indirectes connues RewriterConfig , RewriterConfig.Builder RéécrivainConfig Graph rewriting is experimental and subject to change, not covered by any API stability guarantees.  RewriterConfig.Builder Graph rewriting is experimental and subject to change, not covered by any API stability guarantees.

Méthodes publiques

abstrait RewriterConfig.Toggle	getArithmeticOptimization () Arithmetic optimizations (default is ON) e.g.
abstrait entier	getArithmeticOptimizationValue () Arithmetic optimizations (default is ON) e.g.
abstrait RewriterConfig.Toggle	getAutoMixedPrecision () Optimize data types for CUDA (default is OFF).
abstrait RewriterConfig.Toggle	getAutoMixedPrecisionMkl () Optimize data types for MKL (default is OFF).
abstrait entier	getAutoMixedPrecisionMklValue () Optimize data types for MKL (default is OFF).
abstrait entier	getAutoMixedPrecisionValue () Optimize data types for CUDA (default is OFF).
options AutoParallel abstraites	getAutoParallel () Configures AutoParallel optimization passes either through the meta-optimizer or when manually specified through the optimizers field.
abstrait AutoParallelOptionsOrBuilder	getAutoParallelOrBuilder () Configures AutoParallel optimization passes either through the meta-optimizer or when manually specified through the optimizers field.
abstrait RewriterConfig.Toggle	getCommonSubgraphElimination () Common subgraph elimination (default is ON) e.g.
abstrait entier	getCommonSubgraphEliminationValue () Common subgraph elimination (default is ON) e.g.
abstrait RewriterConfig.Toggle	getConstantFolding () Fold constants (default is ON) Statically infer the value of tensors when possible, and materialize the result using constants.
abstrait entier	getConstantFoldingValue () Fold constants (default is ON) Statically infer the value of tensors when possible, and materialize the result using constants.
abstrait RewriterConfig.CpuLayout	getCpuLayoutConversion () CPU Conversion settings between NHCW and NCHW.
abstrait entier	getCpuLayoutConversionValue () CPU Conversion settings between NHCW and NCHW.
résumé RewriterConfig.CustomGraphOptimizer	getCustomOptimizers (index int) list of CustomGraphOptimizers to apply.
abstrait entier	getCustomOptimizersCount () list of CustomGraphOptimizers to apply.
Liste abstraite < RewriterConfig.CustomGraphOptimizer >	getCustomOptimizersList () list of CustomGraphOptimizers to apply.
résumé RewriterConfig.CustomGraphOptimizerOrBuilder	getCustomOptimizersOrBuilder (index int) list of CustomGraphOptimizers to apply.
Liste abstraite <? étend RewriterConfig.CustomGraphOptimizerOrBuilder >	getCustomOptimizersOrBuilderList () list of CustomGraphOptimizers to apply.
abstrait RewriterConfig.Toggle	getDebugStripper () Strips debug-related nodes from the graph (off by default).
abstrait entier	getDebugStripperValue () Strips debug-related nodes from the graph (off by default).
abstrait RewriterConfig.Toggle	getDependencyOptimization () Control dependency optimizations (default is ON).
abstrait entier	getDependencyOptimizationValue () Control dependency optimizations (default is ON).
booléen abstrait	getDisableMetaOptimizer () Disable the entire meta optimizer (off by default).
booléen abstrait	getDisableModelPruning () If true, don't remove unnecessary ops from the graph bool disable_model_pruning = 2;
booléen abstrait	getExperimentalDisableCompressedTensorOptimization () Disable optimizations that assume compressed tensors.
booléen abstrait	getFailOnOptimizerErrors () If true, any optimization pass failing will cause the MetaOptimizer to stop with an error.
abstrait RewriterConfig.Toggle	getFunctionOptimisation () Function optimizations (default is ON).
abstrait entier	getFunctionOptimizationValue () Function optimizations (default is ON).
abstrait RewriterConfig.Toggle	getImplementationSelector () Enable the swap of kernel implementations based on the device placement (default is ON).
abstrait entier	getImplementationSelectorValue () Enable the swap of kernel implementations based on the device placement (default is ON).
résumé VerifierConfig	getInterOptimizerVerifierConfig () VerifierConfig specifying the verifiers to be run after every optimizer.
résumé VerifierConfigOrBuilder	getInterOptimizerVerifierConfigOrBuilder () VerifierConfig specifying the verifiers to be run after every optimizer.
abstrait RewriterConfig.Toggle	getLayoutOptimizer () Optimize tensor layouts (default is ON) e.g.
abstrait entier	getLayoutOptimizerValue () Optimize tensor layouts (default is ON) e.g.
abstrait RewriterConfig.Toggle	getLoopOptimisation () Loop optimizations (default is ON).
abstrait entier	getLoopOptimizationValue () Loop optimizations (default is ON).
abstrait RewriterConfig.MemOptType	getMemoryOptimization () Configures memory optimization passes through the meta-optimizer.
abstrait entier	getMemoryOptimizationValue () Configures memory optimization passes through the meta-optimizer.
Chaîne abstraite	getMemoryOptimizerTargetNodeNameScope () A node name scope for node names which are valid outputs of recomputations.
résumé com.google.protobuf.ByteString	getMemoryOptimizerTargetNodeNameScopeBytes () A node name scope for node names which are valid outputs of recomputations.
abstrait RewriterConfig.NumIterationsType	getMetaOptimizerItérations () Controls how many times we run the optimizers in meta optimizer (default is once).
abstrait entier	getMetaOptimizerIterationsValue () Controls how many times we run the optimizers in meta optimizer (default is once).
abstrait long	getMetaOptimizerTimeoutMs () Maximum number of milliseconds to spend optimizing a single graph before timing out.
abstrait entier	getMinGraphNodes () The minimum number of nodes in a graph to optimizer.
Chaîne abstraite	getOptimizers (index int) If non-empty, will use this as an alternative way to specify a list of optimizations to turn on and the order of the optimizations (replacing the meta-optimizer).
résumé com.google.protobuf.ByteString	getOptimizersBytes (index int) If non-empty, will use this as an alternative way to specify a list of optimizations to turn on and the order of the optimizations (replacing the meta-optimizer).
abstrait entier	getOptimizersCount () If non-empty, will use this as an alternative way to specify a list of optimizations to turn on and the order of the optimizations (replacing the meta-optimizer).
Liste abstraite<String>	getOptimizersList () If non-empty, will use this as an alternative way to specify a list of optimizations to turn on and the order of the optimizations (replacing the meta-optimizer).
abstrait RewriterConfig.Toggle	getPinToHostOptimization () Force small ops onto the CPU (default is OFF).
abstrait entier	getPinToHostOptimizationValue () Force small ops onto the CPU (default is OFF).
résumé VerifierConfig	getPostOptimizationVerifierConfig () VerifierConfig specifying the verifiers to be run at the end, after all optimizers have run.
résumé VerifierConfigOrBuilder	getPostOptimizationVerifierConfigOrBuilder () VerifierConfig specifying the verifiers to be run at the end, after all optimizers have run.
abstrait RewriterConfig.Toggle	getRemapping () Remapping (default is ON) Remap subgraphs onto more efficient implementations.
abstrait entier	getRemappingValue () Remapping (default is ON) Remap subgraphs onto more efficient implementations.
abstrait RewriterConfig.Toggle	getScopedAllocatorOptimization () Try to allocate some independent Op outputs contiguously in order to merge or eliminate downstream Ops (off by default).
abstrait entier	getScopedAllocatorOptimizationValue () Try to allocate some independent Op outputs contiguously in order to merge or eliminate downstream Ops (off by default).
résumé ScopedAllocatorOptions	getScopedAllocatorOpts () .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
résumé ScopedAllocatorOptionsOrBuilder	getScopedAllocatorOptsOrBuilder () .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;
abstrait RewriterConfig.Toggle	getShapeOptimization () Shape optimizations (default is ON) Simplify computations made on shapes.
abstrait entier	getShapeOptimizationValue () Shape optimizations (default is ON) Simplify computations made on shapes.
booléen abstrait	hasAutoParallel () Configures AutoParallel optimization passes either through the meta-optimizer or when manually specified through the optimizers field.
booléen abstrait	hasInterOptimizerVerifierConfig () VerifierConfig specifying the verifiers to be run after every optimizer.
booléen abstrait	hasPostOptimizationVerifierConfig () VerifierConfig specifying the verifiers to be run at the end, after all optimizers have run.
booléen abstrait	hasScopedAllocatorOpts () .tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;

Méthodes publiques

résumé public RewriterConfig.Toggle getArithmeticOptimization ()

 Arithmetic optimizations (default is ON)
 e.g. Simplify arithmetic ops; merge ops with same value (like constants).
 

.tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;

public abstract int getArithmeticOptimizationValue ()

 Arithmetic optimizations (default is ON)
 e.g. Simplify arithmetic ops; merge ops with same value (like constants).
 

.tensorflow.RewriterConfig.Toggle arithmetic_optimization = 7;

résumé public RewriterConfig.Toggle getAutoMixedPrecision ()

 Optimize data types for CUDA (default is OFF).
 This will try to use float16 on GPU which is faster.
 Note that this can change the numerical stability of the graph and may
 require the use of loss scaling to maintain model convergence.
 

.tensorflow.RewriterConfig.Toggle auto_mixed_precision = 23;

résumé public RewriterConfig.Toggle getAutoMixedPrecisionMkl ()

 Optimize data types for MKL (default is OFF).
 This will try to use bfloat16 on CPUs, which is faster.
 Note that this can change the numerical stability of the graph.
 

.tensorflow.RewriterConfig.Toggle auto_mixed_precision_mkl = 25;

public abstrait int getAutoMixedPrecisionMklValue ()

 Optimize data types for MKL (default is OFF).
 This will try to use bfloat16 on CPUs, which is faster.
 Note that this can change the numerical stability of the graph.
 

.tensorflow.RewriterConfig.Toggle auto_mixed_precision_mkl = 25;

public abstrait int getAutoMixedPrecisionValue ()

 Optimize data types for CUDA (default is OFF).
 This will try to use float16 on GPU which is faster.
 Note that this can change the numerical stability of the graph and may
 require the use of loss scaling to maintain model convergence.
 

.tensorflow.RewriterConfig.Toggle auto_mixed_precision = 23;

résumé public AutoParallelOptions getAutoParallel ()

 Configures AutoParallel optimization passes either through the
 meta-optimizer or when manually specified through the optimizers field.
 

.tensorflow.AutoParallelOptions auto_parallel = 5;

public abstrait AutoParallelOptionsOrBuilder getAutoParallelOrBuilder ()

 Configures AutoParallel optimization passes either through the
 meta-optimizer or when manually specified through the optimizers field.
 

.tensorflow.AutoParallelOptions auto_parallel = 5;

résumé public RewriterConfig.Toggle getCommonSubgraphElimination ()

 Common subgraph elimination (default is ON)
 e.g. Simplify arithmetic ops; merge ops with same value (like constants).
 

.tensorflow.RewriterConfig.Toggle common_subgraph_elimination = 24;

public abstract int getCommonSubgraphEliminationValue ()

 Common subgraph elimination (default is ON)
 e.g. Simplify arithmetic ops; merge ops with same value (like constants).
 

.tensorflow.RewriterConfig.Toggle common_subgraph_elimination = 24;

résumé public RewriterConfig.Toggle getConstantFolding ()

 Fold constants (default is ON)
 Statically infer the value of tensors when possible, and materialize the
 result using constants.
 

.tensorflow.RewriterConfig.Toggle constant_folding = 3;

public abstrait int getConstantFoldingValue ()

 Fold constants (default is ON)
 Statically infer the value of tensors when possible, and materialize the
 result using constants.
 

.tensorflow.RewriterConfig.Toggle constant_folding = 3;

résumé public RewriterConfig.CpuLayout getCpuLayoutConversion ()

 CPU Conversion settings between NHCW and NCHW.
 

.tensorflow.RewriterConfig.CpuLayout cpu_layout_conversion = 50;

public abstrait int getCpuLayoutConversionValue ()

 CPU Conversion settings between NHCW and NCHW.
 

.tensorflow.RewriterConfig.CpuLayout cpu_layout_conversion = 50;

résumé public RewriterConfig.CustomGraphOptimizer getCustomOptimizers (index int)

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

public abstrait int getCustomOptimizersCount ()

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

Liste abstraite publique < RewriterConfig.CustomGraphOptimizer > getCustomOptimizersList ()

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

résumé public RewriterConfig.CustomGraphOptimizerOrBuilder getCustomOptimizersOrBuilder (index int)

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

Liste des résumés publics<? étend RewriterConfig.CustomGraphOptimizerOrBuilder > getCustomOptimizersOrBuilderList ()

 list of CustomGraphOptimizers to apply.
 

repeated .tensorflow.RewriterConfig.CustomGraphOptimizer custom_optimizers = 200;

public abstrait RewriterConfig.Toggle getDebugStripper ()

 Strips debug-related nodes from the graph (off by default).
 

.tensorflow.RewriterConfig.Toggle debug_stripper = 11;

public abstrait int getDebugStripperValue ()

 Strips debug-related nodes from the graph (off by default).
 

.tensorflow.RewriterConfig.Toggle debug_stripper = 11;

résumé public RewriterConfig.Toggle getDependencyOptimization ()

 Control dependency optimizations (default is ON).
 Remove redundant control dependencies, which may enable other optimization.
 

.tensorflow.RewriterConfig.Toggle dependency_optimization = 8;

public abstract int getDependencyOptimizationValue ()

 Control dependency optimizations (default is ON).
 Remove redundant control dependencies, which may enable other optimization.
 

.tensorflow.RewriterConfig.Toggle dependency_optimization = 8;

public abstrait booléen getDisableMetaOptimizer ()

 Disable the entire meta optimizer (off by default).
 

bool disable_meta_optimizer = 19;

public abstrait booléen getDisableModelPruning ()

 If true, don't remove unnecessary ops from the graph
 

bool disable_model_pruning = 2;

public abstrait booléen getExperimentalDisableCompressedTensorOptimization ()

 Disable optimizations that assume compressed tensors. Note that this flag
 is experimental and may be removed in the future.
 

bool experimental_disable_compressed_tensor_optimization = 26;

public abstrait booléen getFailOnOptimizerErrors ()

 If true, any optimization pass failing will cause the MetaOptimizer to
 stop with an error. By default - or when set to false, failing passes are
 skipped silently.
 

bool fail_on_optimizer_errors = 21;

résumé public RewriterConfig.Toggle getFunctionOptimization ()

 Function optimizations (default is ON).
 

.tensorflow.RewriterConfig.Toggle function_optimization = 10;

public abstract int getFunctionOptimizationValue ()

 Function optimizations (default is ON).
 

.tensorflow.RewriterConfig.Toggle function_optimization = 10;

résumé public RewriterConfig.Toggle getImplementationSelector ()

 Enable the swap of kernel implementations based on the device placement
 (default is ON).
 

.tensorflow.RewriterConfig.Toggle implementation_selector = 22;

public abstract int getImplementationSelectorValue ()

 Enable the swap of kernel implementations based on the device placement
 (default is ON).
 

.tensorflow.RewriterConfig.Toggle implementation_selector = 22;

résumé public VerifierConfig getInterOptimizerVerifierConfig ()

 VerifierConfig specifying the verifiers to be run after every optimizer.
 

.tensorflow.VerifierConfig inter_optimizer_verifier_config = 300;

résumé public VerifierConfigOrBuilder getInterOptimizerVerifierConfigOrBuilder ()

 VerifierConfig specifying the verifiers to be run after every optimizer.
 

.tensorflow.VerifierConfig inter_optimizer_verifier_config = 300;

résumé public RewriterConfig.Toggle getLayoutOptimizer ()

 Optimize tensor layouts (default is ON)
 e.g. This will try to use NCHW layout on GPU which is faster.
 

.tensorflow.RewriterConfig.Toggle layout_optimizer = 1;

public abstrait int getLayoutOptimizerValue ()

 Optimize tensor layouts (default is ON)
 e.g. This will try to use NCHW layout on GPU which is faster.
 

.tensorflow.RewriterConfig.Toggle layout_optimizer = 1;

résumé public RewriterConfig.Toggle getLoopOptimization ()

 Loop optimizations (default is ON).
 

.tensorflow.RewriterConfig.Toggle loop_optimization = 9;

public abstrait int getLoopOptimizationValue ()

 Loop optimizations (default is ON).
 

.tensorflow.RewriterConfig.Toggle loop_optimization = 9;

résumé public RewriterConfig.MemOptType getMemoryOptimization ()

 Configures memory optimization passes through the meta-optimizer. Has no
 effect on manually requested memory optimization passes in the optimizers
 field.
 

.tensorflow.RewriterConfig.MemOptType memory_optimization = 4;

public abstrait int getMemoryOptimizationValue ()

 Configures memory optimization passes through the meta-optimizer. Has no
 effect on manually requested memory optimization passes in the optimizers
 field.
 

.tensorflow.RewriterConfig.MemOptType memory_optimization = 4;

chaîne abstraite publique getMemoryOptimizerTargetNodeNameScope ()

 A node name scope for node names which are valid outputs of recomputations.
 Inputs to nodes that match this scope may be recomputed (subject either to
 manual annotation of those input nodes or to manual annotation and
 heuristics depending on memory_optimization), but the nodes themselves will
 not be recomputed. This matches any sub-scopes as well, meaning the scope
 can appear not just as a top-level scope. For example, if the value is
 "gradients/", the default, it will match node name "gradients/foo",
 "foo/gradients/bar", but not "foo_gradients/"
 

string memory_optimizer_target_node_name_scope = 6;

résumé public com.google.protobuf.ByteString getMemoryOptimizerTargetNodeNameScopeBytes ()

chaîne

 A node name scope for node names which are valid outputs of recomputations.
 Inputs to nodes that match this scope may be recomputed (subject either to
 manual annotation of those input nodes or to manual annotation and
 heuristics depending on memory_optimization), but the nodes themselves will
 not be recomputed. This matches any sub-scopes as well, meaning the scope
 can appear not just as a top-level scope. For example, if the value is
 "gradients/", the default, it will match node name "gradients/foo",
 "foo/gradients/bar", but not "foo_gradients/"
 

string memory_optimizer_target_node_name_scope = 6;

résumé public RewriterConfig.NumIterationsType getMetaOptimizerIterations ()

 Controls how many times we run the optimizers in meta optimizer (default
 is once).
 

.tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;

public abstrait int getMetaOptimizerIterationsValue ()

 Controls how many times we run the optimizers in meta optimizer (default
 is once).
 

.tensorflow.RewriterConfig.NumIterationsType meta_optimizer_iterations = 12;

public abstrait long getMetaOptimizerTimeoutMs ()

 Maximum number of milliseconds to spend optimizing a single graph before
 timing out. If equal to 0 the system picks a default (currently 5 minutes).
 If less than 0 the optimizer will never time out.
 

int64 meta_optimizer_timeout_ms = 20;

public abstrait int getMinGraphNodes ()

 The minimum number of nodes in a graph to optimizer. For smaller graphs,
 optimization is skipped.
 0 means the system picks an appropriate number.
 < 0 means do not skip optimization.
 

int32 min_graph_nodes = 17;

chaîne abstraite publique getOptimizers (index int)

 If non-empty, will use this as an alternative way to specify a list of
 optimizations to turn on and the order of the optimizations (replacing the
 meta-optimizer).
 Of the RewriterConfig options, only the AutoParallel configuration options
 (the auto_parallel field) apply to manually requested optimization passes
 ("autoparallel"). Memory optimization passes ("memory") invoked here are
 not configurable (in contrast to memory optimization passes through the
 meta-optimizer) and act only on manual op annotations.
 Custom optimizers (see custom_optimizers) that are not part of this
 schedule will be run after - in the order that they were specified.
 

repeated string optimizers = 100;

résumé public com.google.protobuf.ByteString getOptimizersBytes (index int)

 If non-empty, will use this as an alternative way to specify a list of
 optimizations to turn on and the order of the optimizations (replacing the
 meta-optimizer).
 Of the RewriterConfig options, only the AutoParallel configuration options
 (the auto_parallel field) apply to manually requested optimization passes
 ("autoparallel"). Memory optimization passes ("memory") invoked here are
 not configurable (in contrast to memory optimization passes through the
 meta-optimizer) and act only on manual op annotations.
 Custom optimizers (see custom_optimizers) that are not part of this
 schedule will be run after - in the order that they were specified.
 

repeated string optimizers = 100;

public abstrait int getOptimizersCount ()

 If non-empty, will use this as an alternative way to specify a list of
 optimizations to turn on and the order of the optimizations (replacing the
 meta-optimizer).
 Of the RewriterConfig options, only the AutoParallel configuration options
 (the auto_parallel field) apply to manually requested optimization passes
 ("autoparallel"). Memory optimization passes ("memory") invoked here are
 not configurable (in contrast to memory optimization passes through the
 meta-optimizer) and act only on manual op annotations.
 Custom optimizers (see custom_optimizers) that are not part of this
 schedule will be run after - in the order that they were specified.
 

repeated string optimizers = 100;

liste abstraite publique<String> getOptimizersList ()

 If non-empty, will use this as an alternative way to specify a list of
 optimizations to turn on and the order of the optimizations (replacing the
 meta-optimizer).
 Of the RewriterConfig options, only the AutoParallel configuration options
 (the auto_parallel field) apply to manually requested optimization passes
 ("autoparallel"). Memory optimization passes ("memory") invoked here are
 not configurable (in contrast to memory optimization passes through the
 meta-optimizer) and act only on manual op annotations.
 Custom optimizers (see custom_optimizers) that are not part of this
 schedule will be run after - in the order that they were specified.
 

repeated string optimizers = 100;

résumé public RewriterConfig.Toggle getPinToHostOptimization ()

 Force small ops onto the CPU (default is OFF).
 

.tensorflow.RewriterConfig.Toggle pin_to_host_optimization = 18;

public abstrait int getPinToHostOptimizationValue ()

 Force small ops onto the CPU (default is OFF).
 

.tensorflow.RewriterConfig.Toggle pin_to_host_optimization = 18;

résumé public VerifierConfig getPostOptimizationVerifierConfig ()

 VerifierConfig specifying the verifiers to be run at the end, after all
 optimizers have run.
 

.tensorflow.VerifierConfig post_optimization_verifier_config = 301;

résumé public VerifierConfigOrBuilder getPostOptimizationVerifierConfigOrBuilder ()

 VerifierConfig specifying the verifiers to be run at the end, after all
 optimizers have run.
 

.tensorflow.VerifierConfig post_optimization_verifier_config = 301;

public abstrait RewriterConfig.Toggle getRemapping ()

 Remapping (default is ON)
 Remap subgraphs onto more efficient implementations.
 

.tensorflow.RewriterConfig.Toggle remapping = 14;

public abstrait int getRemappingValue ()

 Remapping (default is ON)
 Remap subgraphs onto more efficient implementations.
 

.tensorflow.RewriterConfig.Toggle remapping = 14;

résumé public RewriterConfig.Toggle getScopedAllocatorOptimization ()

 Try to allocate some independent Op outputs contiguously in order to
 merge or eliminate downstream Ops (off by default).
 

.tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;

public abstract int getScopedAllocatorOptimizationValue ()

 Try to allocate some independent Op outputs contiguously in order to
 merge or eliminate downstream Ops (off by default).
 

.tensorflow.RewriterConfig.Toggle scoped_allocator_optimization = 15;

résumé public ScopedAllocatorOptions getScopedAllocatorOpts ()

.tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;

résumé public ScopedAllocatorOptionsOrBuilder getScopedAllocatorOptsOrBuilder ()

.tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;

résumé public RewriterConfig.Toggle getShapeOptimization ()

 Shape optimizations (default is ON)
 Simplify computations made on shapes.
 

.tensorflow.RewriterConfig.Toggle shape_optimization = 13;

public abstrait int getShapeOptimizationValue ()

 Shape optimizations (default is ON)
 Simplify computations made on shapes.
 

.tensorflow.RewriterConfig.Toggle shape_optimization = 13;

public abstrait booléen hasAutoParallel ()

 Configures AutoParallel optimization passes either through the
 meta-optimizer or when manually specified through the optimizers field.
 

.tensorflow.AutoParallelOptions auto_parallel = 5;

public abstrait booléen hasInterOptimizerVerifierConfig ()

 VerifierConfig specifying the verifiers to be run after every optimizer.
 

.tensorflow.VerifierConfig inter_optimizer_verifier_config = 300;

public abstrait booléen hasPostOptimizationVerifierConfig ()

 VerifierConfig specifying the verifiers to be run at the end, after all
 optimizers have run.
 

.tensorflow.VerifierConfig post_optimization_verifier_config = 301;

public abstrait booléen hasScopedAllocatorOpts ()

.tensorflow.ScopedAllocatorOptions scoped_allocator_opts = 16;