// If the scalar is out of range, sample another random number.
		if new(big.Int).SetBytes(priv).Cmp(N) >= 0 {
			continue
		}

		x, y = curve.ScalarBaseMult(priv)
	}
	return
}

// Marshal converts a point on the curve into the uncompressed form specified in
// SEC 1, Version 2.0, Section 2.3.3. If the point is not on the curve (or is
// the conventional point at infinity), the behavior is undefined.
//

import org.gradle.api.specs.Spec
import spock.lang.Specification

class ClosureToSpecNotationConverterTest extends Specification {
    private ClosureToSpecNotationConverter converter = new ClosureToSpecNotationConverter(String)

    def "converts closures"() {
        given:
        def spec = parse({ it == 'foo' })

        expect:
        spec.isSatisfiedBy("foo")
        !spec.isSatisfiedBy("FOO")

	}
	r, g, b, a := c.RGBA()

	// Convert from alpha-premultiplied to non-alpha-premultiplied.
	if a != 0 {
		r = (r * 0xffff) / a
		g = (g * 0xffff) / a
		b = (b * 0xffff) / a
	}

	y, u, v := RGBToYCbCr(uint8(r>>8), uint8(g>>8), uint8(b>>8))
	return NYCbCrA{YCbCr{Y: y, Cb: u, Cr: v}, uint8(a >> 8)}
}

// RGBToCMYK converts an RGB triple to a CMYK quadruple.

}

def UniformQuantizedStableHloToTflPass
    : Pass<"uniform-quantized-stablehlo-to-tfl", "mlir::func::FuncOp"> {
  let summary = "Converts StableHLO ops using uniform quantized types to equivalent TFL ops.";
  let constructor = "mlir::odml::CreateUniformQuantizedStableHloToTflPass()";
  let description = [{
    Converts StableHLO ops that accept or return uniform quantized types to
    equivalent ops in the TFLite dialect.
  }];

	kubeadmscheme.Scheme.Default(versionedInitCfg)
	if err := kubeadmscheme.Scheme.Convert(versionedInitCfg, internalcfg, nil); err != nil {
		return nil, err
	}

	kubeadmscheme.Scheme.Default(versionedClusterCfg)
	if err := kubeadmscheme.Scheme.Convert(versionedClusterCfg, &internalcfg.ClusterConfiguration, nil); err != nil {
		return nil, err
	}

// variables instead).
std::unique_ptr<OperationPass<ModuleOp>>
CreateRemoveVariableInitializationByConstPass();

// Creates a pass that converts Tensorflow Xla ops to non-Xla ops.
std::unique_ptr<OperationPass<func::FuncOp>> CreateConvertTfXlaOpToTfOpPass();

// Creates a pass that converts TPU models for CPU by removing TPU related ops
// such as TPUPartitionedCall, TPUReplicatedOp, etc. The TF quantizer does not

/**
 * Utility class for saving Java versions in Eclipse descriptors.
 */
public class EclipseJavaVersionMapper {

    private EclipseJavaVersionMapper() {
    }

    /**
     * Converts the target Java version to its Eclipse-specific representation.
     *
     * @param version the target Java version
     * @return the Eclipse-specific representation of the version
     */

	}
	if err == nil {
		prepareStaticVariables(config)
	}
	return config, err
}

// LoadJoinConfigurationFromFile loads versioned JoinConfiguration from file, converts it to internal, defaults and validates it
func LoadJoinConfigurationFromFile(cfgPath string, opts LoadOrDefaultConfigurationOptions) (*kubeadmapi.JoinConfiguration, error) {
	klog.V(1).Infof("loading configuration from %q", cfgPath)

#include "mlir/IR/Value.h"  // from @llvm-project
#include "mlir/Transforms/DialectConversion.h"  // from @llvm-project

namespace mlir {
namespace odml {
// Converts mhlo.dot_general to tfl.BatchMatMul. Reshape and Transpose ops will
// be inserted to convert to well-formed matrix multiply; i.e., mhlo.dot_general
// -> tfl.batch_matmul(mhlo.transpose(mhlo.reshape(operand)), ...).
// Note:

// module in place to the Tensorflow Executor Dialect. Returns an OK Status if
// success, otherwise failure with an error message.
// The Tensorflow Executor Dialect is required to export an MLIR module to a
// Tensorflow GraphDef. This API will add control dependencies and verify that
// the conversion was successful.
//