*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.

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

  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*,

// 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

1.  `envs/*` are lists of variables made with bash syntax. A user must set a
    `TFCI` env param pointing to a list of `env` files.
2.  `utilities/setup.sh`, initialized by all top-level scripts, reads and sets
    values from those `TFCI` paths.
    -   `set -a` / `set -o allexport` exports the variables from `env` files so
        all scripts can use them.
    -   `utilities/setup_docker.sh` creates a container called `tf` with all

"""
contrib = _LazyLoader("contrib", globals(), "tensorflow.contrib",
                      _CONTRIB_WARNING)
# The templated code that replaces the placeholder above sometimes
# sets the __all__ variable. If it does, we have to be sure to add
# "contrib".
if "__all__" in vars():
  vars()["__all__"].append("contrib")

from tensorflow.python.platform import flags

  // Enables running eager ops as functions.
  virtual void SetRunEagerOpAsFunction(bool enable) = 0;

  // Enables rewriting jit_compile functions.
  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.

    TFE_MonitoringSampler2* sampler);
TF_CAPI_EXPORT extern TFE_MonitoringSamplerCell* TFE_MonitoringGetCellSampler2(
    TFE_MonitoringSampler2* sampler, const char* label1, const char* label2);

// Sets whether to use TFRT
TF_CAPI_EXPORT extern void TFE_ContextOptionsSetTfrt(TFE_ContextOptions*,
                                                     bool use_tfrt);

  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;

// `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.