and DoesNotExist.
                            type: string
                          values:
                            description: values is an array of string values. If the
                              operator is In or NotIn, the values array must be non-empty.
                              If the operator is Exists or DoesNotExist, the values

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_translate --tflite-flatbuffer-to-mlir - -o - | FileCheck %s
// Confirm float constants and operators survive a roundtrip

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):
  // CHECK:      [[CONST:%.*]] = "tfl.pseudo_const"() <{value = dense<1.000000e+00> : tensor<4xf32>}> : () -> tensor<4xf32>

	SignalPIDAvailable Signal = "pid.available"
)

// ThresholdOperator is the operator used to express a Threshold.
type ThresholdOperator string

const (
	// OpLessThan is the operator that expresses a less than operator.
	OpLessThan ThresholdOperator = "LessThan"
)

// OpForSignal maps Signals to ThresholdOperators.
// Today, the only supported operator is "LessThan". This may change in the future,

  // flexibility of supporting multiple subtypes in the future.
  variant_tensors:[VariantSubType];
}

// A list of builtin operators. Builtin operators are slightly faster than custom
// ones, but not by much. Moreover, while custom operators accept an opaque
// object containing configuration parameters, builtins have a predetermined
// set of acceptable options.
// LINT.IfChange

// We don't keep a parse tree, just the value of the expression.
type val bool

// exprToken describes a single token in the input.
// Prefix operators define a prefix func that parses the
// upcoming value. Binary operators define an infix func
// that combines two values according to the operator.
// In that case, the parsing loop parses the two values.
type exprToken struct {
	tok    string
	prec   int
	prefix func(*exprParser) val

    llvm::SmallVector<ElementsAttr> res;
    res.reserve(operation_->getNumOperands());
    for (auto opr : operation_->getOperands()) {
      auto op = llvm::dyn_cast<stablehlo::ConstantOp>(opr.getDefiningOp());
      res.push_back(op.getValue());
    }
    return res;
  }

  // Gets a pointer to the operation to be folded.
  Operation* Op() { return operation_; }

 private:

 */
operator fun <U : Task> RegisteringDomainObjectDelegateProviderWithTypeAndAction<out TaskContainer, U>.provideDelegate(
    receiver: Any?,
    property: KProperty<*>
) = ExistingDomainObjectDelegate.of(
    delegateProvider.register(property.name, type.java, action)
)


/**
 * Receiver for the `tasks` block providing an extended set of operators for the configuration of tasks.
 */

			op.checkSuspect(pass, exprs)
		}
	})
	return nil, nil
}

type boolOp struct {
	name  string
	tok   token.Token // token corresponding to this operator
	badEq token.Token // token corresponding to the equality test that should not be used with this operator
}

var (
	or  = boolOp{"or", token.LOR, token.NEQ}
	and = boolOp{"and", token.LAND, token.EQL}
)

// commutativeSets returns all side effect free sets of

#include "tensorflow/core/platform/protobuf.h"
#include "tensorflow/core/platform/tstring.h"

namespace {

// Operators used to create a std::unique_ptr for TF_Tensor and TF_Status
struct TFTensorDeleter {
  void operator()(TF_Tensor* tf_tensor) const { TF_DeleteTensor(tf_tensor); }
};

struct TFStatusDeleter {
  void operator()(TF_Status* tf_status) const { TF_DeleteStatus(tf_status); }
};

 * this class offers:
 *
 *  * **Read/write:** read and write using the same operator.
 *  * **Random access:** access any position within the file.
 *  * **Shared channels:** read and write a file channel that's shared between
 * multiple operators. Note that although the underlying [FileChannel] may be shared,
 * each [FileOperator] should not be.
 */
internal class FileOperator(