size_t proto_len, TF_Status* status) {
  GraphDef g;
  if (!tensorflow::ParseProtoUnlimited(&g, proto, proto_len)) {
    status->status = InvalidArgument("Invalid GraphDef");
    return;
  }
  status->status = s->session->Extend(g);
}

}  // end extern "C"

// Reset helper for converting character arrays to string vectors.

  tensorflow::GraphDef gdef;
  if (!tensorflow::protobuf::TextFormat::ParseFromString(text_proto, &gdef)) {
    status->status = tensorflow::errors::Internal(
        "Invalid text proto for GraphDef: ", text_proto);
    return {};
  }
  const auto& fdef_lib = gdef.library();
  if (fdef_lib.gradient_size() > 0) {
    status->status = tensorflow::errors::Internal(

                          /*expected_max_outputs=*/1, status.get());
  EXPECT_NE(TF_GetCode(status.get()), TF_CANCELLED);
  EXPECT_EQ(TF_GetCode(status.get()), TF_INVALID_ARGUMENT);
  EXPECT_THAT(TF_Message(status.get()), HasSubstr("assertion failed"));

  // Note that future collectives with the same context do not work at the

  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteOp(op);

  // The device of var_handle1 is remote device, which was replaced during
  // cluster update. Removing resource with invalid device should fail
  // gracefully (i.e., with error status) instead of crashing with segfaults.
  op = TFE_NewOp(ctx, "DestroyResourceOp", status);
  ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

};

// Gets tensor from eager tensor handle.
const Tensor* GetTensorFromHandle(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  tensorflow::TensorHandle* handle =
      tensorflow::TensorHandleFromInterface(tensorflow::unwrap(h));
  if (handle->Type() != TensorHandle::LOCAL) {

          " outputs for TPUReplicatedOutput, but got ", expected_outputs));
      TF_SetStatus(status, TF_INVALID_ARGUMENT, message.c_str());
      return result;
    }
    if (absl::holds_alternative<TFE_TensorHandle*>(inputs[0])) {
      TF_SetStatus(status, TF_INVALID_ARGUMENT,
                   "Expected the input to "
                   "TPUReplicatedOutput to be a parallel tensor (placed on the "

    TensorHandlePtr combined_value = CreatePerDeviceValues(
        context.get(), components, device_name, status.get());
    ASSERT_TRUE(TF_GetCode(status.get()) == TF_INVALID_ARGUMENT)
        << TF_Message(status.get());
  }

  {
    // Try to extract the wrong number of components from a parallel tensor
    std::array<TFE_TensorHandle*, 1> correct_components{value_one.get()};

  Status s = RunModel(RecordOperationWithNullGradientFunctionModel, ctx.get(),
                      {x.get()}, absl::MakeSpan(outputs),
                      /*use_function=*/!std::get<2>(GetParam()));
  ASSERT_EQ(error::INVALID_ARGUMENT, s.code());
  ASSERT_EQ(
      "Provided null gradient_function for 'Neg'.\nIf the intent is to treat "
      "this op as non-differentiable consider using RegisterNotDifferentiable "

    TF_CoordinationServiceAgent* agent, TF_Status* status) {
  if (timeout_seconds <= 0) {
    status->status = absl::InvalidArgumentError(
        "TF_CoordinationServiceGetKeyValueWithTimeout invoked with invalid "
        "timeout_seconds <= 0.");
    return nullptr;
  }
  auto* cc_agent = reinterpret_cast<tsl::CoordinationServiceAgent*>(agent);
  auto value = cc_agent->GetKeyValue(std::string_view(key, key_size),

                                          TF_Status* status) {
  tensorflow::FunctionDef fdef;
  bool success = fdef.ParseFromArray(proto, proto_len);
  if (!success) {
    status->status = InvalidArgument(
        "Invalid FunctionDef given to TF_FunctionImportFunctionDef");
    return nullptr;
  }

  TF_Function* func = new TF_Function();
  func->record = new tensorflow::FunctionRecord(std::move(fdef), {}, false);