#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"

namespace tensorflow {

// Converts the given saved_model(either v1 or v2) to a TF Lite FlatBuffer
// string according to the given model flags, toco flags and tags. Returns error
// status if it fails to convert the input.
Status ConvertSavedModelToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,

 * </ul>
 *
 * <p>Using a converter:
 *
 * <ul>
 *   <li>Convert one instance in the "forward" direction using {@code converter.convert(a)}.
 *   <li>Convert multiple instances "forward" using {@code converter.convertAll(as)}.
 *   <li>Convert in the "backward" direction using {@code converter.reverse().convert(b)} or {@code
 *       converter.reverse().convertAll(bs)}.

#include "tensorflow/lite/toco/model_flags.pb.h"
#include "tensorflow/lite/toco/toco_flags.pb.h"

namespace tensorflow {

// Converts the given GraphDef to a TF Lite FlatBuffer string according to the
// given model flags, toco flags and debug information. Returns error status if
// it fails to convert the input.
absl::Status ConvertGraphDefToTFLiteFlatBuffer(
    const toco::ModelFlags& model_flags, toco::TocoFlags& toco_flags,

 * limitations under the License.
 */

package org.gradle.internal.problems.failure;

import org.gradle.internal.service.scopes.Scope;
import org.gradle.internal.service.scopes.ServiceScope;

/**
 * Converts exceptions to their content-based {@link Failure} representation.
 *
 * @see Failure
 */
@ServiceScope(Scope.Global.class)
public interface FailureFactory {

    Failure create(Throwable failure);

    // Delta is not necessarily the same type as start and limit.
    auto abs_cast =
        rewriter.create<mhlo::ConvertOp>(op.getLoc(), compute_type, abs);
    auto delta_cast =
        rewriter.create<mhlo::ConvertOp>(op.getLoc(), compute_type, delta);

    // Compute the total number of integer steps and convert to the HLO
    // dimension tensor.
    auto normalized =

import org.gradle.internal.declarativedsl.parsing.ParseTestUtil.Parser.parseAsTopLevelBlock
import kotlin.test.Test
import kotlin.test.assertEquals


object DomTest {
    @Test
    fun `converts a simple language tree to document`() {
        val tree = parseAsTopLevelBlock(
            """
            myFun {
                a = 1
                b = f("x", z.f("y"))
                c = true

def FloatOrDefaultCompare : Constraint<CPred<
  "!$0 || $0.getValue() == ::mlir::mhlo::ComparisonType::FLOAT">>;
def SameTypeOrDefaultCompare : Constraint<CPred<
  "SameTypeOrDefaultCompare($0, $1)">>;

// Converts a dag of HLOs representing banker rounding (round x.5 to nearest
// even) to tf.round. This only supports float types because mhlo.floor only
// supports float types. tf.round with integer input type will become an

#include "mlir/IR/Types.h"  // from @llvm-project

namespace mlir::quant::stablehlo {

// Returns true if the type or its element type is a float type with bit_width
// > 16.
bool IsLargeFloatType(Type type);

// Converts large float type to bfloat16. Otherwise returns original type.
Type ToBfloat16Type(Type type);

}  // namespace mlir::quant::stablehlo

// Copyright 2020 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

package strconv

// FormatComplex converts the complex number c to a string of the
// form (a+bi) where a and b are the real and imaginary parts,
// formatted according to the format fmt and precision prec.
//
// The format fmt and precision prec have the same meaning as in [FormatFloat].

// 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