StatusOr<Operation*> ConvertOp(
    const tflite::OperatorT& op, const std::vector<Value>& vals_map,
    const std::vector<mlir::TensorType>& intermediate_types,
    Value optional_arg_marker,
    const std::vector<std::unique_ptr<tflite::OperatorCodeT>>& op_codes,
    const std::vector<std::string>& func_names,
    const std::vector<std::unique_ptr<tflite::TensorT>>& tensors, Location loc,
    OpBuilder builder,

  auto model_path = tensorflow::io::JoinPath(*g_test_model_dir, model_name);
  return FlatBufferModel::BuildFromFile(model_path.c_str());
}

template <typename T>
std::vector<T> GetAsVector(const flatbuffers::Vector<T>* vec) {
  return std::vector<T>(vec->begin(), vec->end());
}

void VerifyQuantizationScale(
    const QuantizationParameters& float_quant_params,

      return failure();
    }

    // TensorFlow convolution op only has two inputs, while the TFLite one has
    // three, with the bias vector marked as optional. However, TOCO has a
    // dedicated pass, EnsureBiasVectors, to create default bias vectors for all
    // those missing. So we model TFLite convolution op as requiring three
    // inputs to achieve the legalization task of EnsureBiasVector. this

	}

	return arr
}

// vectorType parses:
//
//	<vector-type> ::= Dv <number> _ <type>
//	              ::= Dv _ <expression> _ <type>
func (st *state) vectorType(isCast bool) AST {
	if len(st.str) == 0 {
		st.fail("expected vector dimension")
	}

	var dim AST
	if st.str[0] == '_' {
		st.advance(1)
		dim = st.expression()
	} else {

build:release_arm64_linux --config=linux_arm64
build:release_arm64_linux --crosstool_top="@ml2014_clang_aarch64_config_aarch64//crosstool:toolchain"
build:release_arm64_linux --config=mkl_aarch64_threadpool
build:release_arm64_linux --copt=-flax-vector-conversions
test:release_arm64_linux --flaky_test_attempts=3

# The old gcc linux build options are preserved in the unsupported_*_linux
# configs. If your project fails to build with Clang, you can use these

    if (!list_element_ty) return failure();

    // Extract tensor elements for the TensorList and construct result type
    // based on the number of elements and element shape.
    const std::vector<tensorflow::Tensor> &tensors = list->tensors();
    llvm::SmallVector<int64_t, 4> result_shape = {
        static_cast<int64_t>(tensors.size())};
    result_shape.append(list_element_ty.getShape().begin(),

		// address such as Unix domain socket path into a valid, ignored
		// Host header (see https://go.dev/issue/61431).
		//
		// We don't preserve the truncation, because sending an altered
		// header field opens a smuggling vector. Instead, zero out the
		// Host header entirely if it isn't valid. (An empty Host is valid;
		// see RFC 9112 Section 3.2.)
		//
		// Return an error if we're sending to a proxy, since the proxy