InvalidArgument("Attribute '", attr_name, "' is not a string");
    return;
  }
  if (max_length <= 0) {
    return;
  }
  const auto& s = attr->s();
  std::memcpy(value, s.data(), std::min<size_t>(s.length(), max_length));
}

void TF_OperationGetAttrStringList(TF_Operation* oper, const char* attr_name,
                                   void** values, size_t* lengths,

        break
      k -= 1

    # A low-effort attempt to find the bazel command that triggered the
    # invocation.
    bazel_comm_min_line_i = (previous_end_line if previous_end_line is not None
                             else 0)
    while k > bazel_comm_min_line_i:
      backtrack_line = log_lines[k]
      # Don't attempt to parse multi-line commands broken up by backslashes

                         TF_Status* status) {
  tensorflow::FunctionDefLibrary lib;
  {
    tensorflow::mutex_lock l(g->mu);
    lib = g->graph.flib_def().ToProto();
  }
  const auto len = std::min(max_func, static_cast<int>(lib.function_size()));
  for (int i = 0; i < len; ++i) {
    TF_Function* func = new TF_Function();
    func->record = new tensorflow::FunctionRecord(lib.function(i), {}, false);
    funcs[i] = func;

  if (!device.empty()) {
    LOG(INFO) << "Setting op Min on device " << device;
  }
  TF_Operation* min = MinWithDevice(feed, one, graph, device, s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

  // Run the graph.
  csession.SetInputs({{feed, Int32Tensor({3, 2, 5})}});
  csession.SetOutputs({min});
  csession.Run(s);
  ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s);

                        char* buffer, TF_Status* status) -> int64_t {
    int64_t bytes_transferred;
    if (offset < buf.size()) {
      size_t bytes_to_copy = std::min<size_t>(buf.size() - offset, n);
      memcpy(buffer, buf.data() + offset, bytes_to_copy);
      bytes_transferred = bytes_to_copy;
    } else {
      bytes_transferred = 0;
    }
    TF_SetStatus(status, TF_OK, "");

}

TEST(CAPI, Execute_MatMul_CPU_Type_Error) {
  Execute_MatMul_CPU_Type_Error(false);
}
TEST(CAPI, Execute_MatMul_CPU_Type_ErrorAsync) {
  Execute_MatMul_CPU_Type_Error(true);
}
TEST(CAPI, Execute_Min_CPU) {
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

    // false, it is safe to keep these flag values as is.
    tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();
    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF);
  }
}

unsigned char TF_SetXlaEnableLazyCompilation(unsigned char enable) {

TF_Operation* Add(TF_Output l, TF_Output r, TF_Graph* graph, TF_Status* s,
                  const char* name = "add");

TF_Operation* Min(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name = "min");

TF_Operation* Mul(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name = "mul");

  TF_SetStatus(status, TF_OK, "");
  const std::vector<std::string>& nodes =
      reinterpret_cast<const tensorflow::grappler::GrapplerItem*>(item)->fetch;

  const int len = std::min(num_values, static_cast<int>(nodes.size()));
  char* p = static_cast<char*>(storage);
  for (int index = 0; index < len; ++index) {
    const std::string& s = nodes[index];
    values[index] = p;

                            const string& op_device, TF_Status* s,
                            const char* name) {
  TF_Operation* op;
  BinaryOpHelper("Min", l, r, graph, s, name, &op, op_device, true);
  return op;
}

TF_Operation* Min(TF_Operation* l, TF_Operation* r, TF_Graph* graph,
                  TF_Status* s, const char* name) {
  return MinWithDevice(l, r, graph, /*op_device=*/"", s, name);
}