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docs/fr/docs/tutorial/path-params.md
En héritant de `str` la documentation sera capable de savoir que les valeurs doivent être de type `string` et pourra donc afficher cette `Enum` correctement. Créez ensuite des attributs de classe avec des valeurs fixes, qui seront les valeurs autorisées pour cette énumération. ```Python hl_lines="1 6-9" {!../../../docs_src/path_params/tutorial005.py!} ``` !!! info
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Thu Apr 18 19:53:19 UTC 2024 - 10K bytes - Viewed (0) -
docs/fr/docs/tutorial/first-steps.md
Vous pouvez aussi retourner des models **Pydantic** (qui seront détaillés plus tard). Il y a de nombreux autres objets et modèles qui seront automatiquement convertis en JSON. Essayez d'utiliser vos favoris, il est fort probable qu'ils soient déjà supportés. ## Récapitulatif * Importez `FastAPI`.
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Wed Sep 27 20:52:31 UTC 2023 - 10.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_ops_a_m.cc
int num = getNext().front().getTerminator()->getNumOperands(); // The number of extra values from the parent ops that should go to `next` // and `finalize`. regions.push_back(RegionSuccessor( &getNext(), getNext().front().getArguments().slice(0, num))); regions.push_back(RegionSuccessor( &getFinalize(), getFinalize().front().getArguments().slice(0, num))); } else {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 146.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/merge_save_function_ops_to_main.cc
} // Gets the GraphOp from the function op. Returns an empty op iff it doesn't // exist. GraphOp GetGraphOpFromFuncOp(func::FuncOp func_op) { if (IsFuncOpEmpty(func_op)) return {}; auto graph_op_range = func_op.front().without_terminator(); if (llvm::hasSingleElement(graph_op_range)) { // The pass runs on a valid tf_executor dialect, so the op should be the // GraphOp. return cast<GraphOp>(graph_op_range.begin()); }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 10.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/transforms/tf2xla_rewriter_test.cc
EXPECT_TRUE(main_func); return main_func; } mlir::Operation& GetFirstOpFromMain() { mlir::func::FuncOp main_func = GetMainFunc(); return main_func.getBody().front().front(); } absl::StatusOr<TupleOp> ImportXlaComputationIntoModule( XlaComputation& computation) { SourceMgrDiagnosticHandler sourceMgrHandler(source_manager_, &context_);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:16:07 UTC 2024 - 11.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/ir/tf_device_ops.td
); let results = (outs Variadic<AnyType>:$results ); let regions = (region SizedRegion<1>:$body); let extraClassDeclaration = [{ Block &GetBody() { return getOperation()->getRegion(0).front(); } bool WrapsSingleOp(); }]; let builders = [ OpBuilder<(ins "StringAttr":$device, "TypeRange":$result_types), [{ $_state.addAttribute("device", device);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jan 23 23:53:20 UTC 2024 - 14.8K bytes - Viewed (0) -
docs/fr/docs/benchmarks.md
En particulier, on voit Uvicorn, Starlette et FastAPI comparés (parmi de nombreux autres outils). Plus le problème résolu par un outil est simple, mieux seront les performances obtenues. Et la plupart des tests de performance ne prennent pas en compte les fonctionnalités additionnelles fournies par les outils. La hiérarchie est la suivante : * **Uvicorn** : un serveur ASGI
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Thu Jul 27 18:49:56 UTC 2023 - 3.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tfrt/analysis/cost_analysis.h
explicit CostAnalysis( mlir::func::FuncOp func_op, const tfrt_stub::CostRecorder* cost_recorder = nullptr) { cost_recorder_ = cost_recorder; AnalyzeArguments(func_op); AnalyzeBlock(&func_op.front()); } int64_t GetCost(mlir::Operation* op) const; private: void AnalyzeArguments(mlir::func::FuncOp func_op); void AnalyzeBlock(mlir::Block* block); void EvaluateCost(mlir::Operation* op);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Jun 06 03:08:33 UTC 2023 - 3.1K bytes - Viewed (0) -
src/runtime/pprof/map.go
if len(e.stk) != len(stk) || e.tag != tag { continue } for j := range stk { if e.stk[j] != uintptr(stk[j]) { continue Search } } // Move to front. if last != nil { last.nextHash = e.nextHash e.nextHash = m.hash[h] m.hash[h] = e } return e } // Add new entry. if len(m.free) < 1 { m.free = make([]profMapEntry, 128) }
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Thu Mar 05 21:51:02 UTC 2020 - 1.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/utils/attribute_utils.h
inline void CopyUnderscoredAttributes(Operation *from, Operation *to) { CopyAttributes(from, to, [](const NamedAttribute &attr) { return attr.getName().strref().front() == '_'; }); } // Copies outside compilation attribute from `from` to `to`. inline void CopyXlaOutsideCompilationAttributes(Operation *from, Operation *to) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 19:47:48 UTC 2024 - 8.7K bytes - Viewed (0)