TFE_DEVICE_PLACEMENT_SILENT_FOR_INT32 = 3,
} TFE_ContextDevicePlacementPolicy;
// LINT.ThenChange(//tensorflow/c/eager/immediate_execution_context.h)

// Sets the default execution mode (sync/async). Note that this can be
// overridden per thread using TFE_ContextSetExecutorForThread.
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetAsync(TFE_ContextOptions*,

  // executor. After that, the value returned by DeviceName will be a full
  // device name such as "/job:localhost/replica:0/task:0/device:GPU:1".
  virtual const string& DeviceName() const = 0;

  // Sets the operation device name.
  //
  // The given `name` must be parseable by DeviceNameUtils::ParseFullName, and
  // the result will be used as a constraint for device placement. See the

#include "tensorflow/c/eager/abstract_context.h"
#include "tensorflow/c/eager/gradients.h"

namespace tensorflow {
namespace gradients {
// Ignores `grad_outputs` and sets all entries in grad_inputs to nullptr.
class NotDifferentiableGradientFunction : public GradientFunction {
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,

// `g` will be returned. Else, an unspecified subset.
//
// If successful, returns the number of TF_Function* successfully set in
// `funcs` and sets status to OK. The caller takes ownership of
// all the returned TF_Functions. They must be deleted with TF_DeleteFunction.
// On error, returns 0, sets status to the encountered error, and the contents
// of funcs will be undefined.

  ~ParallelDevice();

  // Helper to copy a tensor handle from another device once for each component
  // of the ParallelDevice.
  //
  // Sets a bad status and returns a nullptr if `tensor` is already on the
  // ParallelDevice, or if the individual copies fail.
  std::unique_ptr<ParallelTensor> CopyToParallelDevice(TFE_Context* context,

TFE_Op* MinOp(TFE_Context* ctx, TFE_TensorHandle* input,
              TFE_TensorHandle* axis);

// If there is a device of type `device_type`, returns true
// and sets 'device_name' accordingly.
// `device_type` must be either "GPU" or "TPU".
bool GetDeviceName(TFE_Context* ctx, tensorflow::string* device_name,
                   const char* device_type);

   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * Try Google Colab to use TensorFlow.

  return status.ok();
}

// Tests whether a URI scheme results in a filesystem that is supported.
//
// As we need these URI schemes to instantiate the test suite when
// `testing::InitGoogleTest` gets called, here we just store them to an
// internal scheme registry. See `URISchemeRegister` above.
static bool GetURIScheme(const std::string& scheme) {
  tensorflow::SchemeVector()->push_back(scheme);
  return true;

# ==============================================================================
#
# 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

// enabled through MarkForCompilationPassFlags.
TF_CAPI_EXPORT unsigned char TF_GetXlaAutoJitEnabled();

// Sets XLA's minimum cluster size. This has global effect.
TF_CAPI_EXPORT void TF_SetXlaMinClusterSize(int size);

// Gets/Sets TF/XLA flag for whether(true) or not(false) to disable constant
// folding. This is for testing to ensure that XLA is being tested rather than