TF_DeleteGraph);

  TF_SessionOptions* opts = TF_NewSessionOptions();
  TF_Session* sess = TF_NewSession(graph.get(), opts, s);
  TF_DeleteSessionOptions(opts);

  const string gpu_device_name = GPUDeviceName(sess);
  TF_DeleteSession(sess, s);
  CHECK_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);
  return gpu_device_name;
}

    outputs.resize(2, {nullptr, -1});

    // Create loop: while (input1 < input2) input1 += input2 + 1
    TF_Operation* less_than = LessThan(
        params->cond_inputs[0], params->cond_inputs[1], params->cond_graph, s_);
    ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
    params->cond_output = {less_than, 0};

    TF_Operation* add1 = Add(params->body_inputs[0], params->body_inputs[1],

filegroup(
    name = "tensorflow_def_file",
    srcs = [":tf_custom_op_library_additional_deps.dll"],
    output_group = "def_file",
)

# Filter the DEF file to reduce the number of symbols to 64K or less.
# Note that we also write the name of the pyd file into DEF file so that
# the dynamic libraries of custom ops can find it at runtime.
genrule(
    name = "tensorflow_filtered_def_file",

// `protos` and `proto_lens` must point to arrays of length `num_shapes`.
// `protos[i]` must point to an array of `proto_lens[i]` bytes
// representing a binary-serialized TensorShapeProto.
TF_CAPI_EXPORT extern void TF_SetAttrTensorShapeProtoList(
    TF_OperationDescription* desc, const char* attr_name,
    const void* const* protos, const size_t* proto_lens, int num_shapes,
    TF_Status* status);