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docs/tr/docs/features.md
* Kullandığın geliştirme araçları ile iyi çalışır **<abbr title="Integrated Development Environment, kod editörüne benzer">IDE</abbr>/<abbr title="Code errorlarınızı inceleyen program">linter</abbr>/brain**: * Pydantic'in veri yapıları aslında sadece senin tanımladığın classlar; Bu yüzden doğrulanmış dataların ile otomatik tamamlama, linting ve mypy'ı kullanarak sorunsuz bir şekilde çalışabilirsin
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tensorflow/BUILD
# TODO(b/173549186): Move Google-internal TF code out of learning/brain package_group( name = "internal", packages = [ "//devtools/python/indexer/...", "//learning/brain/keras/...", "//learning/brain/mlir/...", "//learning/brain/tfrt/...", "//learning/lib/ami/simple_ml/...", "//learning/pathways/...",
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internal/dsync/drwmutex.go
// for this instance of unlocker, we should let the // caller know that lock is not successfully released // yet. if len(locks)-tolerance == tolerance { // In case of split brain scenarios where // tolerance is exactly half of the len(*locks) // then we need to make sure we have unlocked // upto tolerance+1 - especially for RUnlock // to ensure that we don't end up with active
Go - Registered: Sun Apr 28 19:28:10 GMT 2024 - Last Modified: Thu Feb 22 06:26:06 GMT 2024 - 19.7K bytes - Viewed (0) -
docs/en/docs/features.md
* If you know Python types you know how to use Pydantic. * Plays nicely with your **<abbr title="Integrated Development Environment, similar to a code editor">IDE</abbr>/<abbr title="A program that checks for code errors">linter</abbr>/brain**: * Because pydantic data structures are just instances of classes you define; auto-completion, linting, mypy and your intuition should all work properly with your validated data. * Validate **complex structures**:
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docs/zh/docs/features.md
* **更简单**: * 没有新的模式定义 micro-language 需要学习。 * 如果你知道 Python types,你就知道如何使用 Pydantic。 * 和你 **<abbr title="集成开发环境,和代码编辑器类似">IDE</abbr>/<abbr title="一个检查代码错误的程序">linter</abbr>/brain** 适配: * 因为 pydantic 数据结构仅仅是你定义的类的实例;自动补全,linting,mypy 以及你的直觉应该可以和你验证的数据一起正常工作。 * 验证**复杂结构**: * 使用分层的 Pydantic 模型, Python `typing`的 `List` 和 `Dict` 等等。 * 验证器使我们能够简单清楚的将复杂的数据模式定义、检查并记录为 JSON Schema。
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tensorflow/c/BUILD
# Tests tf_cuda_library( name = "c_test_util", testonly = 1, srcs = ["c_test_util.cc"], hdrs = ["c_test_util.h"], visibility = [ "//learning/brain:__subpackages__", "//tensorflow:__subpackages__", ], deps = [ ":c_api", ":c_api_experimental", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc",
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tensorflow/c/eager/BUILD
], ) tf_cuda_library( name = "c_api_test_util", testonly = 1, srcs = ["c_api_test_util.cc"], hdrs = ["c_api_test_util.h"], visibility = [ "//learning/brain:__subpackages__", "//tensorflow:__subpackages__", ], deps = [ ":c_api", ":c_api_experimental", ":c_api_internal", "//tensorflow/c:c_test_util",
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tensorflow/c/c_api.cc
#include "tensorflow/core/platform/types.h" #include "tensorflow/core/public/session.h" #include "tensorflow/core/public/version.h" // The implementation below is at the top level instead of the // brain namespace because we are defining 'extern "C"' functions. using tensorflow::AttrValueMap; using tensorflow::DataType; using tensorflow::ExtendSessionGraphHelper; using tensorflow::FullTypeDef; using tensorflow::Graph;
C++ - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 102.3K bytes - Viewed (0)