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ci/official/envs/windows_x86

# limitations under the License.
# ==============================================================================
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_IMAGE="gcr.io/tensorflow-testing/tf-win2019-rbe@sha256:1082ef4299a72e44a84388f192ecefc81ec9091c146f507bc36070c089c0edcc"
TFCI_BAZEL_COMMON_ARGS="--repo_env=HERMETIC_PYTHON_VERSION=$TFCI_PYTHON_VERSION"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=windows_x86_cpu

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github.com/tensorflow/tensorflow

ci/official/envs/linux_x86_tpu

TFCI_LIB_SUFFIX="-tpu-linux-x86_64"
TFCI_WHL_BAZEL_TEST_ENABLE=0
TFCI_WHL_IMPORT_TEST_ENABLE=0
TFCI_WHL_SIZE_LIMIT=580M

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github.com/tensorflow/tensorflow

ci/official/utilities/setup.sh

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
#
# Common setup for all TF scripts.
#
# Make as FEW changes to this file as possible. It should not contain utility
# functions (except for tfrun); use dedicated scripts instead and reference them

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github.com/tensorflow/tensorflow

CONTRIBUTING.md

    ```

    This will run it using your installed version of TensorFlow. To be sure
    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

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github.com/tensorflow/tensorflow

tensorflow/c/eager/immediate_execution_distributed_manager.h

  // Set up a multi-client distributed execution environment. Must be called
  // on all tasks in the cluster. This call internally coordinates with other
  // tasks to initialize the eager context and TF server for multi-client
  // execution.
  virtual absl::Status EnableCollectiveOps(const ServerDef& server_def) = 0;

  // Check if the remote task is alive.

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github.com/tensorflow/tensorflow

tensorflow/c/c_api_experimental.cc

        "Unable to find the enqueue node in the TF graph.");
    return;
  }

  TF_Operation* placeholder_op = TF_GraphOperationByName(
      session->graph,
      tensorflow::strings::StrCat("arg_tensor_enqueue_", tensor_id).c_str());
  if (placeholder_op == nullptr) {
    status->status = tensorflow::errors::Internal(
        "Unable to find the placeholder node as input to enqueue in the TF "
        "graph.");
    return;
  }

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github.com/apache/maven

api/pom.xml

            <artifactId>maven-javadoc-plugin</artifactId>
            <configuration>
              <tags>
                <tag>
                  <name>provisional</name>
                  <placement>tf</placement>
                  <head>Provisional:</head>
                </tag>
              </tags>
            </configuration>
            <reportSets>
              <reportSet>
                <id>aggregate</id>

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- Last Modified: Sat Oct 19 18:11:20 UTC 2024

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github.com/tensorflow/tensorflow

tensorflow/c/c_api_macros_internal.h

#ifdef __cplusplus
#include "tensorflow/core/platform/status.h"

// Macro to verify that the field `struct_size` of STRUCT_OBJ is initialized.
// `struct_size` is used for struct member compatibility check between core TF
// and plug-ins with the same C API minor version. More info here:
// https://github.com/tensorflow/community/blob/master/rfcs/20200612-stream-executor-c-api/C_API_versioning_strategy.md

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github.com/tensorflow/tensorflow

ci/official/containers/linux_arm64/setup.sources.sh

#
# setup.python.sh: Install a specific Python version and packages for it.
# Usage: setup.python.sh <pyversion> <requirements.txt>

# Sets up custom apt sources for our TF images.

# Prevent apt install tzinfo from asking our location (assumes UTC)
export DEBIAN_FRONTEND=noninteractive

# Set up shared custom sources
apt-get update
apt-get install -y gnupg ca-certificates

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github.com/tensorflow/tensorflow

tensorflow/c/eager/dlpack.cc

#endif
  } else {
    status->status = tensorflow::errors::InvalidArgument(
        "Unsupported Device Type for dlpack");
  }

  return ctx;
}

// Converts DLDevice to TF device name.
absl::optional<std::string> DeviceNameFromDlContext(const DLDevice& ctx,
                                                    TF_Status* status) {
  switch (ctx.device_type) {
    case DLDeviceType::kDLCPU:

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