}

void TFE_ContextGetFunctionDef(TFE_Context* ctx, const char* function_name,
                               TF_Buffer* buf, TF_Status* status) {
  auto* function_def = tensorflow::unwrap(ctx)->FindFunctionDef(function_name);
  if (function_def == nullptr) {
    status->status = tensorflow::errors::NotFound(
        "Unable to find FunctionDef with name: ", function_name);
    return;
  }

                         status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  const char* function_name = "test_reduce_mul";
  RegisterCollectiveMulFunction(context.get(), function_name, 2, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());

  TensorHandlePtr value_one(FloatTensorHandle(7., status.get()));

bool GetAttrValue(TF_Operation* oper, const char* attr_name,
                  tensorflow::AttrValue* attr_value, TF_Status* s);

// Returns a sorted vector of std::pair<function_name, gradient_func> from
// graph_def.library().gradient()
std::vector<std::pair<string, string>> GetGradDefs(
    const tensorflow::GraphDef& graph_def);

// Returns a sorted vector of names contained in `grad_def`

                                                     const char* function_name,
                                                     TF_Buffer* buf,
                                                     TF_Status* status);

// Get GraphDebugInfo containing stack traces mapping to node names
TF_CAPI_EXPORT extern void TFE_ContextGetGraphDebugInfo(
    TFE_Context* ctx, const char* function_name, TF_Buffer* buf,
    TF_Status* status);

    const tensorflow::GraphDef& graph_def) {
  std::vector<std::pair<string, string>> grads;
  for (const tensorflow::GradientDef& grad : graph_def.library().gradient()) {
    grads.emplace_back(grad.function_name(), grad.gradient_func());
  }
  std::sort(grads.begin(), grads.end());
  return grads;
}

std::vector<string> GetFuncNames(const tensorflow::GraphDef& graph_def) {
  std::vector<string> names;

 public:
  FunctionErrorInjectionPass(string error_node, string error_device)
      : error_node_(error_node), error_device_(error_device) {}
  absl::Status Run(const std::string& function_name,
                   const tensorflow::DeviceSet& device_set,
                   const tensorflow::ConfigProto& config_proto,
                   const FunctionOptions& function_options,

    val couldBeExtensionFunction = paramCount > 0
    val paramCountWithReceiver = paramCount - 1
    val functionFqName = "$qualifiedBaseName.${method.name}"

    return collectDescendantsOfType { ktFunction ->
        // Name check
        if (ktFunction.fqName?.asString() != functionFqName) {
            return@collectDescendantsOfType false
        }

        // Preliminary extension function check

	// Used during evaluation for aggregation funcs
	aggregate *aggVal
}

// SimpleArgFunc represents functions with simple expression
// arguments.
type SimpleArgFunc struct {
	FunctionName string `parser:" @(\"AVG\" | \"MAX\" | \"MIN\" | \"SUM\" |  \"COALESCE\" | \"NULLIF\" | \"TO_STRING\" | \"TO_TIMESTAMP\" | \"UTCNOW\" | \"CHAR_LENGTH\" | \"CHARACTER_LENGTH\" | \"LOWER\" | \"UPPER\") "`

          path: Path,
          functionName: String,
          parameterName: String,
        ): Path {
          events.add("$functionName:$path")
          return path
        }

        override fun onPathResult(
          path: Path,
          functionName: String,
        ): Path {
          events.add("$functionName:$path")
          return path
        }
      }

	errNonTimestampArg   = errors.New("Expected a timestamp argument")
)

func (e *FuncExpr) getFunctionName() FuncName {
	switch {
	case e.SFunc != nil:
		return FuncName(strings.ToUpper(e.SFunc.FunctionName))
	case e.Count != nil:
		return aggFnCount
	case e.Cast != nil:
		return sqlFnCast
	case e.Substring != nil:
		return sqlFnSubstring
	case e.Extract != nil:
		return sqlFnExtract
	case e.Trim != nil: