# 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",

      }
    }
  }

  private fun readAndWriteFrames() {
    check(socket == null)
    val socket = serverSocket!!.accept()!!
    this.socket = socket

    // Bail out now if this instance was closed while waiting for the socket to accept.
    synchronized(this) {
      if (executor.isShutdown) {
        socket.close()
        return
      }
    }

#
# This file is autogenerated by pip-compile with Python 3.10
# by the following command:
#
#    bazel run //ci/official/requirements_updater:requirements.update
#
absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \

#
# This file is autogenerated by pip-compile with Python 3.12
# by the following command:
#
#    bazel run //ci/official/requirements_updater:requirements.update
#
absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \

#
# This file is autogenerated by pip-compile with Python 3.12
# by the following command:
#
#    bazel run //ci/official/requirements_updater:requirements.update
#
absl-py==2.1.0 \
    --hash=sha256:526a04eadab8b4ee719ce68f204172ead1027549089702d99b9059f129ff1308 \
    --hash=sha256:7820790efbb316739cde8b4e19357243fc3608a152024288513dd968d7d959ff
    # via
    #   keras-nightly
    #   tb-nightly
astor==0.7.1 \

researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.

TensorFlow provides stable [Python](https://www.tensorflow.org/api_docs/python)

  // +optional
  optional string unhealthyPodEvictionPolicy = 4;
}

// PodDisruptionBudgetStatus represents information about the status of a
// PodDisruptionBudget. Status may trail the actual state of a system.
message PodDisruptionBudgetStatus {
  // Most recent generation observed when updating this PDB status. DisruptionsAllowed and other

    * 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**:

* <abbr title='Zadania wykonywane w tle, bez zatrzymywania żądań, w tym samym procesie. Po angielsku: In-process background tasks'>Zadania w tle</abbr>.
* Eventy startup i shutdown.
* Klient testowy zbudowany na bazie biblioteki `requests`.
* **CORS**, GZip, pliki statyczne, streamy.
* Obsługa **sesji i ciasteczek**.
* 100% pokrycie testami.
* 100% adnotacji typów.

## Cechy Pydantic

* 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