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tensorflow/compiler/mlir/lite/stablehlo/transforms/legalize_hlo_conversions/reduce.cc
return success(); } // Pattern matches the following reduction function for ArgMax/ArgMin: // %0 = compare{GT}(%lhs_value, %rhs_value) // %1 = compare{NE}(%lhs_value, %lhs_value) // %2 = or(%0, %1) // %3 = select(%2, %lhs_value, %rhs_value) // %4 = compare{EQ}(%lhs_value, %rhs_value) // %5 = compare{LT}(%lhs_index, %rhs_index) // %6 = and(%4, %5) // %7 = or(%2, %6) // %8 = select(%7, %lhs_index, %rhs_index)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 20:53:17 UTC 2024 - 8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/decompose_reduce_dataset.mlir
// CHECK-NEXT: %[[HAS_VALUE:[0-9]*]] = "tf.OptionalHasValue"(%[[GET_NEXT]]) // CHECK-NEXT: %[[IF:.*]]:2 = "tf.IfRegion"(%[[HAS_VALUE]]) // CHECK-NEXT: %[[GET_VALUE:[0-9]*]]:2 = "tf.OptionalGetValue"(%[[GET_NEXT]]) // CHECK-NEXT: %[[FUNC_CALL:[0-9]*]]:2 = func.call @__reduce_func_3(%[[ARG_11]], %[[ARG_12]], %[[GET_VALUE]]#0, %[[GET_VALUE]]#1, %[[ARG_13]], %[[ARG_14]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Aug 18 17:16:34 UTC 2022 - 9.8K bytes - Viewed (0) -
docs/de/docs/tutorial/dependencies/sub-dependencies.md
async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]): return {"fresh_value": fresh_value} ``` === "Python 3.8+ nicht annotiert" !!! tip "Tipp" Bevorzugen Sie die `Annotated`-Version, falls möglich. ```Python hl_lines="1" async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Sat Mar 30 18:09:48 UTC 2024 - 6.3K bytes - Viewed (0) -
docs/en/docs/tutorial/dependencies/sub-dependencies.md
```Python hl_lines="1" async def needy_dependency(fresh_value: Annotated[str, Depends(get_value, use_cache=False)]): return {"fresh_value": fresh_value} ``` === "Python 3.8+ non-Annotated" !!! tip Prefer to use the `Annotated` version if possible. ```Python hl_lines="1" async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)):
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Sat May 18 23:43:13 UTC 2024 - 5.6K bytes - Viewed (0) -
docs/em/docs/tutorial/dependencies/sub-dependencies.md
🏧 😐 🌐❔ 👆 💭 👆 💪 🔗 🤙 🔠 🔁 (🎲 💗 🕰) 🎏 📨 ↩️ ⚙️ "💾" 💲, 👆 💪 ⚒ 🔢 `use_cache=False` 🕐❔ ⚙️ `Depends`: ```Python hl_lines="1" async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)): return {"fresh_value": fresh_value} ``` ## 🌃 ↖️ ⚪️➡️ 🌐 🎀 🔤 ⚙️ 📥, **🔗 💉** ⚙️ 🙅. 🔢 👈 👀 🎏 *➡ 🛠️ 🔢*. ✋️, ⚫️ 📶 🏋️, & ✔ 👆 📣 🎲 🙇 🐦 🔗 "📊" (🌲). !!! tip
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Sat Apr 01 09:26:04 UTC 2023 - 3.3K bytes - Viewed (0) -
docs/zh/docs/tutorial/dependencies/sub-dependencies.md
在高级使用场景中,如果不想使用「缓存」值,而是为需要在同一请求的每一步操作(多次)中都实际调用依赖项,可以把 `Depends` 的参数 `use_cache` 的值设置为 `False` : ```Python hl_lines="1" async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)): return {"fresh_value": fresh_value} ``` ## 小结 千万别被本章里这些花里胡哨的词藻吓倒了,其实**依赖注入**系统非常简单。 依赖注入无非是与*路径操作函数*一样的函数罢了。 但它依然非常强大,能够声明任意嵌套深度的「图」或树状的依赖结构。 !!! tip "提示"
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Sat May 14 11:59:59 UTC 2022 - 3.2K bytes - Viewed (0) -
docs/ja/docs/tutorial/dependencies/sub-dependencies.md
高度なシナリオでは、「キャッシュされた」値を使うのではなく、同じリクエストの各ステップ(おそらく複数回)で依存関係を呼び出す必要があることがわかっている場合、`Depens`を使用する際に、`use_cache=False`というパラメータを設定することができます。 ```Python hl_lines="1" async def needy_dependency(fresh_value: str = Depends(get_value, use_cache=False)): return {"fresh_value": fresh_value} ``` ## まとめ ここで使われている派手な言葉は別にして、**依存性注入** システムは非常にシンプルです。 *path operation関数*と同じように見えるただの関数です。 しかし、それでも非常に強力で、任意の深くネストされた依存関係「グラフ」(ツリー)を宣言することができます。
Registered: Mon Jun 17 08:32:26 UTC 2024 - Last Modified: Mon Jan 15 16:43:41 UTC 2024 - 4.4K bytes - Viewed (0) -
tensorflow/c/experimental/saved_model/core/revived_types/variable.cc
TensorShape shape, absl::optional<std::string> name, ImmediateTensorHandlePtr handle) : TensorHandleConvertible(std::move(handle)), name_(name.has_value() ? *name : "Variable"), dtype_(dtype), shape_(shape), ctx_(ctx) {} Variable::~Variable() { // If the handle is null (perhaps because variable was std::moved from), then
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Oct 08 20:55:40 UTC 2020 - 4.4K bytes - Viewed (0) -
tensorflow/compiler/aot/tests/make_test_graphs.py
def tfvariable_readonly(_): x = variables.Variable(1000.0, name='x') unused_y = variables.Variable(1000.0, name='y') old_x = x.value() with ops.control_dependencies([old_x]): new_value = math_ops.add(old_x, 42.0) array_ops.identity(new_value, name='result') # TODO(b/147908587): Change x and the two constants back to have a scalar shape # when the bug is fixed. def tfvariable(_):
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Jun 15 15:25:23 UTC 2023 - 7.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/rewrite_util.h
DenseElementsAttr GetScalarOfType(Type ty, T raw_value) { RankedTensorType scalar_ty = RankedTensorType::get({}, ty); if (auto float_ty = mlir::dyn_cast<FloatType>(ty)) { FloatAttr attr = FloatAttr::get(float_ty, raw_value); return DenseElementsAttr::get(scalar_ty, attr); } else if (auto int_ty = mlir::dyn_cast<IntegerType>(ty)) { IntegerAttr attr = IntegerAttr::get(int_ty, raw_value); return DenseElementsAttr::get(scalar_ty, attr);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 22 19:47:48 UTC 2024 - 4K bytes - Viewed (0)