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(

                                      int ndim) {
  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;
    // Note that dimensions with size=1 can have any stride.
    if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) {
      valid = false;
    }
    expected_stride *= shape_arr[i];

  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);

    status->status = tensorflow::errors::InvalidArgument(
        "Invalid tensorflow.ServerDef protocol buffer");
    return;
  } else if (context->GetContextId() ==
             tensorflow::EagerContext::kInvalidContextId) {
    status->status = tensorflow::errors::InvalidArgument(
        "Trying to update a context with invalid context id.");
  }
  status->status =

  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return nullptr;
  }
  return tensorflow::unwrap(h)->DeviceType(&status->status);
}

int TFE_TensorHandleDeviceID(TFE_TensorHandle* h, TF_Status* status) {
  if (h == nullptr) {
    status->status = tensorflow::errors::InvalidArgument("Invalid handle");
    return -1;
  }

  int real_num_outputs = expected_max_outputs;
  TFE_OpSetCancellationManager(op_.get(), wrap(cancellation_manager_), status);
  if (TF_GetCode(status) != TF_OK) return;

  // unwrap op_ and set step_id only if valid step id value was set.
  // Currently only required for non-TFRT use cases, e.g., EagerOp.
  if (step_id_.has_value()) {
    tensorflow::unwrap(op_.get())->SetStepId(step_id_.value());
  }

  TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Try to set an invalid shape (cannot change 2x3 to a 2x5).
  dims[1] = 5;
  TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s);
  EXPECT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(s)) << TF_Message(s);

  // Test for a scalar.