using tsl::StatusOr;

// Converts the TensorFlow DataType 'dtype' into an MLIR (scalar) type.
Status ConvertDataType(DataType dtype, mlir::Builder builder, mlir::Type* type);

// Converts a scalar MLIR type to a TensorFlow Datatype.
Status ConvertScalarTypeToDataType(mlir::Type type, DataType* dtype);

// Converts an MLIR type to TensorFlow DataType. If 'type' is a scalar type, it

import spock.lang.Specification

class JavaUtilCollectionStrategyTest extends Specification {
    def store = new DefaultModelSchemaStore(DefaultModelSchemaExtractor.withDefaultStrategies())

    def "assembles schema for a Set of scalar type"() {
        expect:
        def schema = store.getSchema(ModelTypes.set(ModelType.of(String)))
        schema instanceof ScalarCollectionSchema
        schema instanceof ManagedImplSchema

import (
	"crypto/subtle"
)

// A dynamic lookup table for variable-base, constant-time scalar muls.
type projLookupTable struct {
	points [8]projCached
}

// A precomputed lookup table for fixed-base, constant-time scalar muls.
type affineLookupTable struct {
	points [8]affineCached
}

// A dynamic lookup table for variable-base, variable-time scalar muls.
type nafLookupTable5 struct {
	points [8]projCached
}

#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

// Return a tensor handle containing a float scalar
TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, float value);

// Return a tensor handle containing a int scalar
TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, int value);

// Return a tensor handle containing a bool scalar
TFE_TensorHandle* TestScalarTensorHandle(TFE_Context* ctx, bool value);

  // CHECK-NEXT:     [[SUB:%.*]] = tfl.sub %{{.*}}, %{{.*}} {fused_activation_function = "RELU6"} : tensor<3x2xi32>
  // CHECK-NEXT:     [[SCALAR:%.*]] = "tfl.pseudo_const"() <{value = dense<10> : tensor<i32>}> : () -> tensor<i32>
  // CHECK-NEXT:     [[ADD:%.*]] = tfl.add([[SCALAR]], [[SUB]]) {fused_activation_function = "NONE"} : (tensor<i32>, tensor<3x2xi32>) -> tensor<3x2xi32>
  // CHECK-NEXT:     return [[ADD]] : tensor<3x2xi32>

var parser *typed.Parser
var schemaYAML = typed.YAMLObject(`types:
- name: __untyped_atomic_
  scalar: untyped
  list:
    elementType:
      namedType: __untyped_atomic_
    elementRelationship: atomic
  map:
    elementType:
      namedType: __untyped_atomic_
    elementRelationship: atomic
- name: __untyped_deduced_
  scalar: untyped
  list:
    elementType:
      namedType: __untyped_atomic_

// count. It is a tensor<1xi32>, and we use R1 instead of a scalar because it is
// easier to concat it with other offsets.
TensorType GetSizeType(OpBuilder builder);

// Reshapes a scalar value to match the size type tensor<i32>.
Value ReshapeScalarToSizeType(OpBuilder builder, Value scalar, Location loc);

// Creates ops that represent the indices of the slice for an element in the

		[6]*argField{ap_CondRegField_6_8, ap_VecSReg_29_29_11_15, ap_VecSReg_30_30_16_20}},
	{XSCMPEXPQP, 0xfc0007fe00000000, 0xfc00014800000000, 0x60000100000000, // VSX Scalar Compare Exponents Quad-Precision X-form (xscmpexpqp BF,VRA,VRB)
		[6]*argField{ap_CondRegField_6_8, ap_VecReg_11_15, ap_VecReg_16_20}},

                ModelType<?> innerType = propertyType.getTypeVariables().get(0);
                s.append(String.format("Its property '%s %s' is not a valid scalar collection%n", propertyType.getName(), propertyName));
                s.append(String.format("A scalar collection can not contain '%s's%n", innerType));
                s.append(explainScalarCollections(scalarTypes));
            } else {

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