import java.io.File
import java.lang.reflect.InvocationTargetException


/**
 * An optimised interpreter for the Kotlin DSL based on the idea of
 * [partial evaluation](https://en.wikipedia.org/wiki/Partial_evaluation).
 *
 * Instead of interpreting a given Kotlin DSL script directly, the interpreter emits a
 * specialized program that captures the optimal execution procedure for the particular

         kTfLiteOk);
  QCHECK(interpreter->AllocateTensors() == kTfLiteOk);
  QCHECK(interpreter->Invoke() == kTfLiteOk);

  // Print the resulting outputs.
  // TODO(jpienaar): Allow specifying output stream/file.
  QCHECK(interpreter->outputs().size() ==
         main.getFunctionType().getNumResults());
  for (int index : interpreter->outputs()) {
    const auto& out = *interpreter->tensor(index);
    // Print name if named.

// call invocations.
var FindRegexOptimization = &interpreter.RegexOptimization{
	Function:   "find",
	RegexIndex: 1,
	Factory: func(call interpreter.InterpretableCall, regexPattern string) (interpreter.InterpretableCall, error) {
		compiledRegex, err := regexp.Compile(regexPattern)
		if err != nil {
			return nil, err
		}

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,
    python_version = TF_PYTHON_VERSION,
)

load("@python//:defs.bzl", "interpreter")
load("@rules_python//python:pip.bzl", "pip_parse")

pip_parse(
    name = "pypi",
    python_interpreter_target = interpreter,
    requirements = "//:requirements_lock_" + TF_PYTHON_VERSION.replace(".", "_") + ".txt",
)

load("@pypi//:requirements.bzl", "install_deps")

if x%GOBUILDEXIT%==x1...

            scriptHandler.dependencies,
            "classpath"
        )
    }

    private
    val interpreter by lazy {
        Interpreter(InterpreterHost(gradlePropertiesController))
    }

    inner class InterpreterHost(
        gradleProperties: GradlePropertiesController,
    ) : Interpreter.Host {

        override val compilerOptions: KotlinCompilerOptions =

    <command value="run" />
    <scripts>
      <script value="runIrTestInNode" />
    </scripts>
    <arguments value="$FilePath$" />
    <node-interpreter value="project" />
    <envs />
    <method v="2" />
  </configuration>

// RUN: tf-opt --mlir-print-debuginfo --canonicalize --tfl-while-loop-outline %s | mlir-tflite-runner --dump-interpreter-state 2>&1 | FileCheck %s

// Verify value computed:
// ----------------------
// CHECK: result: Tensor<type: FLOAT32, shape: 1, values: 96>
// CHECK: pconst: Tensor<type: INT32, shape: , values: 1>

// Verify tensors in interpreter state:
// ------------------------------------

     * The first element of the list is the command interpreter (sh or cmd depending on the platform).
     * The list also contains a path to the script relative to {@code baseDir}.
     * This means that {@code baseDir} has to be used as a current dir when executing the command,
     * otherwise interpreter won't be able to find the script file.
     *

load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION")

python_register_toolchains(
    name = "python",
    ignore_root_user_error = True,
    python_version = TF_PYTHON_VERSION,
)

load("@python//:defs.bzl", "interpreter")
load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse")

NUMPY_ANNOTATIONS = {
    "numpy": package_annotation(
        additive_build_content = """\
filegroup(
    name = "includes",