TFE_ContextOptionsSetDevicePlacementPolicy(opts, global_policy);
  TFE_Context* ctx = TFE_NewContext(opts, status.get());
  if (thread_policy != global_policy) {
    TFE_ContextSetThreadLocalDevicePlacementPolicy(ctx, thread_policy);
  }
  TFE_DeleteContextOptions(opts);
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

// ops.
TF_CAPI_EXPORT extern void TFE_ContextClearCaches(TFE_Context* ctx);

// Sets a thread-local device placement policy. After this call, other calls to
// TFE_Execute in the same thread will use the device policy specified here
// instead of the device policy used to construct the context. This has no
// effect on the device policy used by other program threads.
TF_CAPI_EXPORT extern void TFE_ContextSetThreadLocalDevicePlacementPolicy(

// Sets a custom Executor for the current thread. All nodes created by this
// thread will be added to this Executor. It will override the current executor.
TF_CAPI_EXPORT extern void TFE_ContextSetExecutorForThread(TFE_Context*,
                                                           TFE_Executor*);

// Returns the Executor for the current thread.
TF_CAPI_EXPORT extern TFE_Executor* TFE_ContextGetExecutorForThread(

case "${VERSION}" in
devtoolset-9)
  wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 "https://vault.centos.org/centos/7/sclo/Source/rh/devtoolset-9-gcc-9.3.1-2.2.el7.src.rpm"
  rpm2cpio "devtoolset-9-gcc-9.3.1-2.2.el7.src.rpm" |cpio -idmv
  tar -xvf "gcc-9.3.1-20200408.tar.xz" --strip 1
  ;;
devtoolset-10)

#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/platform/str_util.h"
#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/stringpiece.h"
#include "tensorflow/core/platform/thread_annotations.h"
#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

# Contributing guidelines

## Pull Request Checklist

Before sending your pull requests, make sure you do the following:

-   Read the [contributing guidelines](CONTRIBUTING.md).
-   Read the [Code of Conduct](CODE_OF_CONDUCT.md).
-   Ensure you have signed the
    [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-   Check if your changes are consistent with the

  virtual void SetJitCompileRewrite(bool enable) = 0;

  // Sets the device placement policy for the current thread.
  virtual void SetThreadLocalDevicePlacementPolicy(
      ContextDevicePlacementPolicy policy) = 0;
  // Returns the device placement policy for the current thread.
  virtual ContextDevicePlacementPolicy GetDevicePlacementPolicy() const = 0;

  // Configure graph collection in RunMetadata.

      static_cast<tensorflow::ContextDevicePlacementPolicy>(policy));
}

// Note: this function looks up a thread local policy. So it should be called in
// the appropriate client thread. In particular, in async mode, it may not be
// safe to call this function from the async EagerExecutor threads.
extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy(
    TFE_Context* ctx) {

  - An explanation of who can exploit this vulnerability, and what they gain
    when doing so. Write an attack scenario that demonstrates how your issue
    violates the use cases and security assumptions defined in the threat model.
    This will help us evaluate your report quickly, especially if the issue is
    complex.
  - Whether this vulnerability is public or known to third parties. If it is,
    please provide details.

        should switch to
        `tf.estimator.Estimator.experimental_export_all_saved_models`.
    *   Update `regression_head` to the new Head API for Canned Estimator V2.
    *   Switch `multi_class_head` to Head API for Canned Estimator V2.
    *   Replace all occurrences of `tf.contrib.estimator.InMemoryEvaluatorHook`
        and `tf.contrib.estimator.make_stop_at_checkpoint_step_hook` with