*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*,

  // 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

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

    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);

#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,