PassManager pm(&ctx);
  // Intermediate output from QuantizePass will have quantized ops
  // (XlaCallModuleOps) with quantized input and output types, which are not
  // allowed in the TF dialect.
  pm.enableVerifier(false);

  PrepareQuantizePassOptions options;
  options.enable_per_channel_quantized_weight_ =
      enable_per_channel_quantized_weight_;
  // Change this to user-given bit width once we have custom configuration.

    ModuleOp module, llvm::StringRef module_name) {
  PassManager tf_to_executor(module.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(tf_to_executor);
  tf_to_executor.enableVerifier();
  AddTfDialectToExecutorPasses(tf_to_executor);

  if (VLOG_IS_ON(1) ||
      DEBUG_DATA_DUMPER()->ShouldDump(module_name.str(), kDebugGroupMain)) {
    ::tensorflow::DumpMlirOpToFile(

    ModuleOp module, llvm::StringRef module_name) {
  PassManager tf_to_executor(module.getContext());
  ::tensorflow::applyTensorflowAndCLOptions(tf_to_executor);
  tf_to_executor.enableVerifier();

  AddTfDialectToExecutorPasses(tf_to_executor);

  mlir::StatusScopedDiagnosticHandler diag_handler(
      module.getContext(), /*propagate=*/false,
      /*filter_stack=*/!VLOG_IS_ON(1));

        "1; this could happen if the binary doesn't link the constant fold "
        "hook registration library.");
  }

  PassManager bridge(module.getContext());
  bridge.enableVerifier();
  ::tensorflow::applyTensorflowAndCLOptions(bridge);

  // Populate a passmanager with the list of passes that implement the bridge.
  pipeline_builder(bridge);

  mlir::StatusScopedDiagnosticHandler diag_handler(

        "1; this could happen if the binary doesn't link the constant fold "
        "hook registration library.");
  }

  PassManager bridge(module.getContext());
  bridge.enableVerifier();
  ::tensorflow::applyTensorflowAndCLOptions(bridge);

  // Populate a passmanager with the list of passes that implement the bridge.
  pipeline_builder(bridge);

  // quantized input and output types, which are not allowed in TF dialect.
  // This can be removed when the composite call supports quantized types.
  pm.enableVerifier(false);

  QuantizationSpecs quant_specs;
  quant_specs.inference_type = tensorflow::DT_QINT8;
  quant_specs.disable_per_channel = !enable_per_channel_quantization_;

org.gradle.caching=true
org.gradle.jvmargs='-Dfile.encoding=UTF-8'
org.gradle.parallel=true
android.enableJetifier=true
android.useAndroidX=true
kotlin.mpp.stability.nowarn=true
kotlin.js.compiler=ir
kotlin.incremental.js.ir=true
androidBuild=false
graalBuild=false
loomBuild=false
containerTests=false

# https://developer.android.com/topic/libraries/support-library/androidx-rn
android.useAndroidX=true
# Automatically convert third-party libraries to use AndroidX

                        versionCode 1
                    }
                }
        """
        file("gradle.properties") << """
            android.useAndroidX=true
            android.enableJetifier=true
        """.stripIndent()
        file("src/main/AndroidManifest.xml") << """<?xml version="1.0" encoding="utf-8"?>
            <manifest xmlns:android="http://schemas.android.com/apk/res/android"