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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/...",
Plain Text - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Apr 09 18:15:11 GMT 2024 - 53.4K bytes - Viewed (8) -
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/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 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 102.3K bytes - Viewed (0) -
internal/grid/grid_test.go
<-serverSent // Now do 100 other requests to ensure that the server doesn't block. for i := 0; i < 100; i++ { _, err := remoteConn.Request(ctx, handlerTest2, []byte(testPayload)) errFatal(err) } // Drain responses got := 0 for resp := range st.responses { // t.Log("got response", resp) errFatal(resp.Err) if resp.Msg[0] != byte(got) { t.Error("expected response", got, "got", resp.Msg[0]) }
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RELEASE.md
used to shard the checkpoint with a maximum shard file size. Users with advanced use cases can also write their own custom `tf.train.experimental.ShardingCallback`s. * `tf.train.CheckpointOptions` * Added `experimental_skip_slot_variables` (a boolean option) to skip restoring of optimizer slot variables in a checkpoint. * `tf.saved_model.SaveOptions`
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CHANGELOG/CHANGELOG-1.29.md
- Added `kubectl node drain` helper callbacks `OnPodDeletionOrEvictionStarted` and `OnPodDeletionOrEvictionFailed`; people extending `kubectl` can use these new callbacks for more granularity. Deprecated the `OnPodDeletedOrEvicted`
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cmd/bucket-replication.go
client := &http.Client{ Transport: globalRemoteTargetTransport, Timeout: 10 * time.Second, } resp, err := client.Do(req) if err != nil { return err } if err == nil { // Drain the connection. xhttp.DrainBody(resp.Body) } if resp != nil { amzid := resp.Header.Get(xhttp.AmzRequestHostID) if _, ok := globalNodeNamesHex[amzid]; ok { return BucketRemoteIdenticalToSource{
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