return tensorflow::errors::Unimplemented(
      "SetAttrFunction has not been implemented yet.");
}
Status TapeOperation::SetAttrFunctionName(const char* attr_name,
                                          const char* value, size_t length) {
  return tensorflow::errors::Unimplemented(
      "SetAttrFunctionName has not been implemented "
      "yet.");
}
Status TapeOperation::SetAttrTensor(const char* attr_name,

      TF_GUARDED_BY(mu);

  // The keys of this map are all the active sessions using this graph. Each
  // value records whether the graph has been mutated since the corresponding
  // session has been run (this is detected in RecordMutation function). If the
  // string is empty, no mutation has occurred. Otherwise the string is a
  // description of the mutation suitable for returning to the user.
  //

      immediate_execution_ctx_.get(), {X.get(), Y.get()}, UseFunction()));
}

TEST_P(CppGradients, TestBiasAddGrad) {
  if (UseFunction() && UseMlir()) {
    GTEST_SKIP() << "SetAttrString has not been implemented yet.\n";
  }

  // A
  float A_vals[] = {1.0f, 2.0f, 3.0f, 4.0f};
  int64_t A_dims[] = {2, 2};
  AbstractTensorHandlePtr A;
  {
    AbstractTensorHandle* A_raw;

    int num_retvals = 1;
    TFE_Execute(matmul, &retvals[0], &num_retvals, status.get());
    ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

    // The CPU handle should have been copied and have a mirror on the GPU
    ASSERT_TRUE(cpu_arg->HasLocalMirror(gpu_device));

    TFE_DeleteOp(matmul);
    TFE_DeleteTensorHandle(retvals[0]);
    TFE_DeleteTensorHandle(hgpu);
  }

// (or, if none is supplied, all existing schemes are used). As a scheme can
// become available after a shared object with a filesystem implementation is
// loaded, we can only check for availability after all arguments have been
// parsed.
//
// Furthermore, as `INSTANTIATE_TEST_SUITE_P` needs to be at global level and we
// don't want to have a `std::vector<std::string>` at global level, we use a

// things like the available devices, resource manager etc.
// TFE_Context must outlive all tensor handles created using it. In other
// words, TFE_DeleteContext() must be called after all tensor handles have
// been deleted (with TFE_DeleteTensorHandle).
//
// TODO(ashankar): Merge with TF_Session?
typedef struct TFE_Context TFE_Context;

TF_CAPI_EXPORT extern TFE_Context* TFE_NewContext(

  if (entry != GetFactories().end()) {
    default_factory = GetFactories().find(name)->second;
    return absl::OkStatus();
  }
  string msg = absl::StrCat(
      "No tracing engine factory has been registered with the key '", name,
      "' (available: ");
  // Ensure deterministic (sorted) order in the error message
  std::set<string> factories_sorted;
  for (const auto& factory : GetFactories())

  return tensorflow::errors::Unimplemented(
      "SetAttrFunction has not been implemented yet.");
}
Status SetAttrFunctionName(AbstractOperation* op_, const char* attr_name,
                           const char* value, size_t length,
                           ForwardOperation* forward_op_) {
  return tensorflow::errors::Unimplemented(
      "SetAttrFunctionName has not been implemented "
      "yet.");
}

      // name collisions only if this prefix has not been provided explicitly
      // by the user. If it was provided, assert that it remained intact.
      if (prefix != nullptr && !absl::StartsWith(n->name(), prefix_cmp)) {
        status->status = tensorflow::errors::Internal(
            "BUG: The gradients prefix have been unexpectedly altered when "

      )
   ```

4) Add the version to `SUPPORTED_VERSIONS` in `updater.sh`, and
   `release_updater.sh`

5) Run the `updater.sh` shell script. \
   If the base requirements file hasn't yet been updated to account for the new
   Python version, which will require different versions for at least some
   dependencies, it will need to be updated now, for the script to run
   successfully.