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.

RUN /setup.python.sh python3.10 devel.requirements.txt
RUN /setup.python.sh python3.11 devel.requirements.txt
RUN /setup.python.sh python3.12 devel.requirements.txt
# "python3" commands by default run under 3.10

#ifdef __cplusplus
} /* end extern "C" */
#endif

#ifdef __cplusplus
// A workaround to ease conversion to and from numpy objects and
// TFE_TensorHandle's.
//
// TODO(ashankar): Figure out an alternative scheme that precludes the need for
// these API-boundary breaking methods.
namespace tensorflow {
class Tensor;
}  // namespace tensorflow

TFE_TensorHandle* TFE_NewTensorHandle(const tensorflow::Tensor& t,

        from the old optimizer. These API changes are mostly related to
        getting rid of slot variables and TF1 support. Please check the API
        documentation to find alternatives to the missing API. If you must
        call the deprecated API, please change your optimizer to the legacy
        optimizer.
    *   **Learning rate schedule access.** When using a `LearningRateSchedule`,

      return result;
    }
    if (!item) {
      TF_SetStatusFromGCSStatus(item.status(), status);
      return result;
    }
    auto value = *std::move(item);
    std::string children = std::holds_alternative<std::string>(value)
                               ? std::get<std::string>(value)
                               : std::get<gcs::ObjectMetadata>(value).name();
    auto pos = children.find(prefix);
    if (pos != 0) {

      return result;
    }
    std::vector<TensorHandlePtr> components;
    components.reserve(inputs.size());
    for (int i = 0; i < inputs.size(); ++i) {
      if (absl::holds_alternative<ParallelTensor*>(inputs[i])) {
        std::string message(absl::StrCat(
            "Expected all inputs to TPUReplicatedInput to be non-parallel "
            "TensorHandles. The input ",
            i,

  worker_server1.release();
  worker_server2.release();
}

TEST(CAPI, DistributedFunctionNoError) {
  TestDistributedFunctionCancellation(false);
}

// TODO(b/170399182): Update test once an alternative to using the function
// optimization hook is in place.
TEST(CAPI, DISABLED_DistributedFunctionCancelledOnError) {
  TestDistributedFunctionCancellation(true);
}