| // Code generated by protoc-gen-go. DO NOT EDIT. |
| // source: google/cloud/automl/v1beta1/classification.proto |
| |
| package automl // import "google.golang.org/genproto/googleapis/cloud/automl/v1beta1" |
| |
| import proto "github.com/golang/protobuf/proto" |
| import fmt "fmt" |
| import math "math" |
| import _ "google.golang.org/genproto/googleapis/api/annotations" |
| |
| // Reference imports to suppress errors if they are not otherwise used. |
| var _ = proto.Marshal |
| var _ = fmt.Errorf |
| var _ = math.Inf |
| |
| // This is a compile-time assertion to ensure that this generated file |
| // is compatible with the proto package it is being compiled against. |
| // A compilation error at this line likely means your copy of the |
| // proto package needs to be updated. |
| const _ = proto.ProtoPackageIsVersion2 // please upgrade the proto package |
| |
| // Type of the classification problem. |
| type ClassificationType int32 |
| |
| const ( |
| // Should not be used, an un-set enum has this value by default. |
| ClassificationType_CLASSIFICATION_TYPE_UNSPECIFIED ClassificationType = 0 |
| // At most one label is allowed per example. |
| ClassificationType_MULTICLASS ClassificationType = 1 |
| // Multiple labels are allowed for one example. |
| ClassificationType_MULTILABEL ClassificationType = 2 |
| ) |
| |
| var ClassificationType_name = map[int32]string{ |
| 0: "CLASSIFICATION_TYPE_UNSPECIFIED", |
| 1: "MULTICLASS", |
| 2: "MULTILABEL", |
| } |
| var ClassificationType_value = map[string]int32{ |
| "CLASSIFICATION_TYPE_UNSPECIFIED": 0, |
| "MULTICLASS": 1, |
| "MULTILABEL": 2, |
| } |
| |
| func (x ClassificationType) String() string { |
| return proto.EnumName(ClassificationType_name, int32(x)) |
| } |
| func (ClassificationType) EnumDescriptor() ([]byte, []int) { |
| return fileDescriptor_classification_e3eb78fdd10472f9, []int{0} |
| } |
| |
| // Contains annotation details specific to classification. |
| type ClassificationAnnotation struct { |
| // Output only. A confidence estimate between 0.0 and 1.0. A higher value |
| // means greater confidence that the annotation is positive. If a user |
| // approves an annotation as negative or positive, the score value remains |
| // unchanged. If a user creates an annotation, the score is 0 for negative or |
| // 1 for positive. |
| Score float32 `protobuf:"fixed32,1,opt,name=score,proto3" json:"score,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ClassificationAnnotation) Reset() { *m = ClassificationAnnotation{} } |
| func (m *ClassificationAnnotation) String() string { return proto.CompactTextString(m) } |
| func (*ClassificationAnnotation) ProtoMessage() {} |
| func (*ClassificationAnnotation) Descriptor() ([]byte, []int) { |
| return fileDescriptor_classification_e3eb78fdd10472f9, []int{0} |
| } |
| func (m *ClassificationAnnotation) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ClassificationAnnotation.Unmarshal(m, b) |
| } |
| func (m *ClassificationAnnotation) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ClassificationAnnotation.Marshal(b, m, deterministic) |
| } |
| func (dst *ClassificationAnnotation) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ClassificationAnnotation.Merge(dst, src) |
| } |
| func (m *ClassificationAnnotation) XXX_Size() int { |
| return xxx_messageInfo_ClassificationAnnotation.Size(m) |
| } |
| func (m *ClassificationAnnotation) XXX_DiscardUnknown() { |
| xxx_messageInfo_ClassificationAnnotation.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ClassificationAnnotation proto.InternalMessageInfo |
| |
| func (m *ClassificationAnnotation) GetScore() float32 { |
| if m != nil { |
| return m.Score |
| } |
| return 0 |
| } |
| |
| // Model evaluation metrics for classification problems. |
| // Visible only to v1beta1 |
| type ClassificationEvaluationMetrics struct { |
| // Output only. The Area under precision recall curve metric. |
| AuPrc float32 `protobuf:"fixed32,1,opt,name=au_prc,json=auPrc,proto3" json:"au_prc,omitempty"` |
| // Output only. The Area under precision recall curve metric based on priors. |
| BaseAuPrc float32 `protobuf:"fixed32,2,opt,name=base_au_prc,json=baseAuPrc,proto3" json:"base_au_prc,omitempty"` |
| // Output only. Metrics that have confidence thresholds. |
| // Precision-recall curve can be derived from it. |
| ConfidenceMetricsEntry []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry `protobuf:"bytes,3,rep,name=confidence_metrics_entry,json=confidenceMetricsEntry,proto3" json:"confidence_metrics_entry,omitempty"` |
| // Output only. Confusion matrix of the evaluation. |
| // Only set for MULTICLASS classification problems where number |
| // of labels is no more than 10. |
| // Only set for model level evaluation, not for evaluation per label. |
| ConfusionMatrix *ClassificationEvaluationMetrics_ConfusionMatrix `protobuf:"bytes,4,opt,name=confusion_matrix,json=confusionMatrix,proto3" json:"confusion_matrix,omitempty"` |
| // Output only. The annotation spec ids used for this evaluation. |
| AnnotationSpecId []string `protobuf:"bytes,5,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ClassificationEvaluationMetrics) Reset() { *m = ClassificationEvaluationMetrics{} } |
| func (m *ClassificationEvaluationMetrics) String() string { return proto.CompactTextString(m) } |
| func (*ClassificationEvaluationMetrics) ProtoMessage() {} |
| func (*ClassificationEvaluationMetrics) Descriptor() ([]byte, []int) { |
| return fileDescriptor_classification_e3eb78fdd10472f9, []int{1} |
| } |
| func (m *ClassificationEvaluationMetrics) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ClassificationEvaluationMetrics.Unmarshal(m, b) |
| } |
| func (m *ClassificationEvaluationMetrics) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ClassificationEvaluationMetrics.Marshal(b, m, deterministic) |
| } |
| func (dst *ClassificationEvaluationMetrics) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ClassificationEvaluationMetrics.Merge(dst, src) |
| } |
| func (m *ClassificationEvaluationMetrics) XXX_Size() int { |
| return xxx_messageInfo_ClassificationEvaluationMetrics.Size(m) |
| } |
| func (m *ClassificationEvaluationMetrics) XXX_DiscardUnknown() { |
| xxx_messageInfo_ClassificationEvaluationMetrics.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ClassificationEvaluationMetrics proto.InternalMessageInfo |
| |
| func (m *ClassificationEvaluationMetrics) GetAuPrc() float32 { |
| if m != nil { |
| return m.AuPrc |
| } |
| return 0 |
| } |
| |
| func (m *ClassificationEvaluationMetrics) GetBaseAuPrc() float32 { |
| if m != nil { |
| return m.BaseAuPrc |
| } |
| return 0 |
| } |
| |
| func (m *ClassificationEvaluationMetrics) GetConfidenceMetricsEntry() []*ClassificationEvaluationMetrics_ConfidenceMetricsEntry { |
| if m != nil { |
| return m.ConfidenceMetricsEntry |
| } |
| return nil |
| } |
| |
| func (m *ClassificationEvaluationMetrics) GetConfusionMatrix() *ClassificationEvaluationMetrics_ConfusionMatrix { |
| if m != nil { |
| return m.ConfusionMatrix |
| } |
| return nil |
| } |
| |
| func (m *ClassificationEvaluationMetrics) GetAnnotationSpecId() []string { |
| if m != nil { |
| return m.AnnotationSpecId |
| } |
| return nil |
| } |
| |
| // Metrics for a single confidence threshold. |
| type ClassificationEvaluationMetrics_ConfidenceMetricsEntry struct { |
| // Output only. The confidence threshold value used to compute the metrics. |
| ConfidenceThreshold float32 `protobuf:"fixed32,1,opt,name=confidence_threshold,json=confidenceThreshold,proto3" json:"confidence_threshold,omitempty"` |
| // Output only. Recall under the given confidence threshold. |
| Recall float32 `protobuf:"fixed32,2,opt,name=recall,proto3" json:"recall,omitempty"` |
| // Output only. Precision under the given confidence threshold. |
| Precision float32 `protobuf:"fixed32,3,opt,name=precision,proto3" json:"precision,omitempty"` |
| // Output only. The harmonic mean of recall and precision. |
| F1Score float32 `protobuf:"fixed32,4,opt,name=f1_score,json=f1Score,proto3" json:"f1_score,omitempty"` |
| // Output only. The recall when only considering the label that has the |
| // highest prediction score and not below the confidence threshold for each |
| // example. |
| RecallAt1 float32 `protobuf:"fixed32,5,opt,name=recall_at1,json=recallAt1,proto3" json:"recall_at1,omitempty"` |
| // Output only. The precision when only considering the label that has the |
| // highest predictionscore and not below the confidence threshold for each |
| // example. |
| PrecisionAt1 float32 `protobuf:"fixed32,6,opt,name=precision_at1,json=precisionAt1,proto3" json:"precision_at1,omitempty"` |
| // Output only. The harmonic mean of [recall_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1] and [precision_at1][google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1]. |
| F1ScoreAt1 float32 `protobuf:"fixed32,7,opt,name=f1_score_at1,json=f1ScoreAt1,proto3" json:"f1_score_at1,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Reset() { |
| *m = ClassificationEvaluationMetrics_ConfidenceMetricsEntry{} |
| } |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) ProtoMessage() {} |
| func (*ClassificationEvaluationMetrics_ConfidenceMetricsEntry) Descriptor() ([]byte, []int) { |
| return fileDescriptor_classification_e3eb78fdd10472f9, []int{1, 0} |
| } |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfidenceMetricsEntry.Unmarshal(m, b) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfidenceMetricsEntry.Marshal(b, m, deterministic) |
| } |
| func (dst *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ClassificationEvaluationMetrics_ConfidenceMetricsEntry.Merge(dst, src) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_Size() int { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfidenceMetricsEntry.Size(m) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) XXX_DiscardUnknown() { |
| xxx_messageInfo_ClassificationEvaluationMetrics_ConfidenceMetricsEntry.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ClassificationEvaluationMetrics_ConfidenceMetricsEntry proto.InternalMessageInfo |
| |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetConfidenceThreshold() float32 { |
| if m != nil { |
| return m.ConfidenceThreshold |
| } |
| return 0 |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecall() float32 { |
| if m != nil { |
| return m.Recall |
| } |
| return 0 |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecision() float32 { |
| if m != nil { |
| return m.Precision |
| } |
| return 0 |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1Score() float32 { |
| if m != nil { |
| return m.F1Score |
| } |
| return 0 |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetRecallAt1() float32 { |
| if m != nil { |
| return m.RecallAt1 |
| } |
| return 0 |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetPrecisionAt1() float32 { |
| if m != nil { |
| return m.PrecisionAt1 |
| } |
| return 0 |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfidenceMetricsEntry) GetF1ScoreAt1() float32 { |
| if m != nil { |
| return m.F1ScoreAt1 |
| } |
| return 0 |
| } |
| |
| // Confusion matrix of the model running the classification. |
| type ClassificationEvaluationMetrics_ConfusionMatrix struct { |
| // Output only. IDs of the annotation specs used in the confusion matrix. |
| AnnotationSpecId []string `protobuf:"bytes,1,rep,name=annotation_spec_id,json=annotationSpecId,proto3" json:"annotation_spec_id,omitempty"` |
| // Output only. Rows in the confusion matrix. The number of rows is equal to |
| // the size of `annotation_spec_id`. |
| // `row[i].value[j]` is the number of examples that have ground truth of the |
| // `annotation_spec_id[i]` and are predicted as `annotation_spec_id[j]` by |
| // the model being evaluated. |
| Row []*ClassificationEvaluationMetrics_ConfusionMatrix_Row `protobuf:"bytes,2,rep,name=row,proto3" json:"row,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix) Reset() { |
| *m = ClassificationEvaluationMetrics_ConfusionMatrix{} |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*ClassificationEvaluationMetrics_ConfusionMatrix) ProtoMessage() {} |
| func (*ClassificationEvaluationMetrics_ConfusionMatrix) Descriptor() ([]byte, []int) { |
| return fileDescriptor_classification_e3eb78fdd10472f9, []int{1, 1} |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix.Unmarshal(m, b) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix.Marshal(b, m, deterministic) |
| } |
| func (dst *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix.Merge(dst, src) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_Size() int { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix.Size(m) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix) XXX_DiscardUnknown() { |
| xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix proto.InternalMessageInfo |
| |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix) GetAnnotationSpecId() []string { |
| if m != nil { |
| return m.AnnotationSpecId |
| } |
| return nil |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix) GetRow() []*ClassificationEvaluationMetrics_ConfusionMatrix_Row { |
| if m != nil { |
| return m.Row |
| } |
| return nil |
| } |
| |
| // Output only. A row in the confusion matrix. |
| type ClassificationEvaluationMetrics_ConfusionMatrix_Row struct { |
| // Output only. Value of the specific cell in the confusion matrix. |
| // The number of values each row is equal to the size of |
| // annotatin_spec_id. |
| ExampleCount []int32 `protobuf:"varint,1,rep,packed,name=example_count,json=exampleCount,proto3" json:"example_count,omitempty"` |
| XXX_NoUnkeyedLiteral struct{} `json:"-"` |
| XXX_unrecognized []byte `json:"-"` |
| XXX_sizecache int32 `json:"-"` |
| } |
| |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) Reset() { |
| *m = ClassificationEvaluationMetrics_ConfusionMatrix_Row{} |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) String() string { |
| return proto.CompactTextString(m) |
| } |
| func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) ProtoMessage() {} |
| func (*ClassificationEvaluationMetrics_ConfusionMatrix_Row) Descriptor() ([]byte, []int) { |
| return fileDescriptor_classification_e3eb78fdd10472f9, []int{1, 1, 0} |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Unmarshal(b []byte) error { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix_Row.Unmarshal(m, b) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Marshal(b []byte, deterministic bool) ([]byte, error) { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix_Row.Marshal(b, m, deterministic) |
| } |
| func (dst *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Merge(src proto.Message) { |
| xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix_Row.Merge(dst, src) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_Size() int { |
| return xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix_Row.Size(m) |
| } |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) XXX_DiscardUnknown() { |
| xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix_Row.DiscardUnknown(m) |
| } |
| |
| var xxx_messageInfo_ClassificationEvaluationMetrics_ConfusionMatrix_Row proto.InternalMessageInfo |
| |
| func (m *ClassificationEvaluationMetrics_ConfusionMatrix_Row) GetExampleCount() []int32 { |
| if m != nil { |
| return m.ExampleCount |
| } |
| return nil |
| } |
| |
| func init() { |
| proto.RegisterType((*ClassificationAnnotation)(nil), "google.cloud.automl.v1beta1.ClassificationAnnotation") |
| proto.RegisterType((*ClassificationEvaluationMetrics)(nil), "google.cloud.automl.v1beta1.ClassificationEvaluationMetrics") |
| proto.RegisterType((*ClassificationEvaluationMetrics_ConfidenceMetricsEntry)(nil), "google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry") |
| proto.RegisterType((*ClassificationEvaluationMetrics_ConfusionMatrix)(nil), "google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix") |
| proto.RegisterType((*ClassificationEvaluationMetrics_ConfusionMatrix_Row)(nil), "google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row") |
| proto.RegisterEnum("google.cloud.automl.v1beta1.ClassificationType", ClassificationType_name, ClassificationType_value) |
| } |
| |
| func init() { |
| proto.RegisterFile("google/cloud/automl/v1beta1/classification.proto", fileDescriptor_classification_e3eb78fdd10472f9) |
| } |
| |
| var fileDescriptor_classification_e3eb78fdd10472f9 = []byte{ |
| // 583 bytes of a gzipped FileDescriptorProto |
| 0x1f, 0x8b, 0x08, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0xff, 0xac, 0x54, 0xcf, 0x4f, 0xdb, 0x3e, |
| 0x14, 0xff, 0xa6, 0xa5, 0xe5, 0xcb, 0x83, 0x8d, 0xca, 0x30, 0x94, 0x75, 0x6c, 0x54, 0x70, 0xa9, |
| 0xd0, 0x94, 0x10, 0x76, 0xdc, 0x29, 0x64, 0x45, 0x8a, 0x54, 0x58, 0x95, 0x94, 0x03, 0xbb, 0x58, |
| 0xae, 0xeb, 0x86, 0x48, 0x69, 0x1c, 0x39, 0x0e, 0x85, 0xeb, 0xee, 0xfb, 0xdb, 0xf6, 0x5f, 0xec, |
| 0xef, 0x98, 0x62, 0x87, 0x74, 0x95, 0x2a, 0x36, 0x69, 0xbb, 0xe5, 0x7d, 0x7e, 0xf9, 0x3d, 0xbf, |
| 0xc8, 0x70, 0x16, 0x71, 0x1e, 0x25, 0xcc, 0xa6, 0x09, 0x2f, 0xa6, 0x36, 0x29, 0x24, 0x9f, 0x27, |
| 0xf6, 0xbd, 0x33, 0x61, 0x92, 0x38, 0x36, 0x4d, 0x48, 0x9e, 0xc7, 0xb3, 0x98, 0x12, 0x19, 0xf3, |
| 0xd4, 0xca, 0x04, 0x97, 0x1c, 0xbd, 0xd1, 0x0e, 0x4b, 0x39, 0x2c, 0xed, 0xb0, 0x2a, 0x47, 0xf7, |
| 0xb0, 0x8a, 0x23, 0x59, 0x6c, 0x93, 0x34, 0xe5, 0x52, 0x39, 0x73, 0x6d, 0x3d, 0x3e, 0x03, 0xd3, |
| 0x5b, 0x89, 0x74, 0x6b, 0x09, 0xda, 0x87, 0x56, 0x4e, 0xb9, 0x60, 0xa6, 0xd1, 0x33, 0xfa, 0x8d, |
| 0x40, 0x17, 0xc7, 0x3f, 0xda, 0x70, 0xb4, 0x6a, 0x19, 0xdc, 0x93, 0xa4, 0x50, 0x5f, 0x57, 0x4c, |
| 0x8a, 0x98, 0xe6, 0xe8, 0x15, 0xb4, 0x49, 0x81, 0x33, 0x41, 0x9f, 0xac, 0xa4, 0x18, 0x09, 0x8a, |
| 0xde, 0xc1, 0xf6, 0x84, 0xe4, 0x0c, 0x57, 0x5c, 0x43, 0x71, 0x5b, 0x25, 0xe4, 0x2a, 0xfe, 0x9b, |
| 0x01, 0x26, 0xe5, 0xe9, 0x2c, 0x9e, 0xb2, 0x94, 0x32, 0x3c, 0xd7, 0x69, 0x98, 0xa5, 0x52, 0x3c, |
| 0x9a, 0xcd, 0x5e, 0xb3, 0xbf, 0x7d, 0x1e, 0x5a, 0xcf, 0xcc, 0x6a, 0xfd, 0xa6, 0x2f, 0xcb, 0xab, |
| 0xc3, 0x2b, 0x64, 0x50, 0x46, 0x07, 0x07, 0x74, 0x2d, 0x8e, 0x16, 0xd0, 0x29, 0x99, 0x22, 0x8f, |
| 0x79, 0x8a, 0xe7, 0x44, 0x8a, 0xf8, 0xc1, 0xdc, 0xe8, 0x19, 0xfd, 0xed, 0xf3, 0xe1, 0x5f, 0xb7, |
| 0xa1, 0x42, 0xaf, 0x54, 0x66, 0xb0, 0x4b, 0x57, 0x01, 0xf4, 0x1e, 0xd0, 0x72, 0x55, 0x38, 0xcf, |
| 0x18, 0xc5, 0xf1, 0xd4, 0x6c, 0xf5, 0x9a, 0xfd, 0xad, 0xa0, 0xb3, 0x64, 0xc2, 0x8c, 0x51, 0x7f, |
| 0xda, 0xfd, 0xda, 0x80, 0x83, 0xf5, 0x93, 0x21, 0x07, 0xf6, 0x7f, 0xb9, 0x50, 0x79, 0x27, 0x58, |
| 0x7e, 0xc7, 0x93, 0x69, 0xb5, 0x96, 0xbd, 0x25, 0x37, 0x7e, 0xa2, 0xd0, 0x01, 0xb4, 0x05, 0xa3, |
| 0x24, 0x49, 0xaa, 0xfd, 0x54, 0x15, 0x3a, 0x84, 0xad, 0x4c, 0x30, 0x1a, 0x97, 0x6d, 0x9a, 0x4d, |
| 0xbd, 0xba, 0x1a, 0x40, 0xaf, 0xe1, 0xff, 0x99, 0x83, 0xf5, 0xef, 0xb2, 0xa1, 0xc8, 0xcd, 0x99, |
| 0x13, 0x96, 0x25, 0x7a, 0x0b, 0xa0, 0x23, 0x30, 0x91, 0x8e, 0xd9, 0xd2, 0x4e, 0x8d, 0xb8, 0xd2, |
| 0x41, 0x27, 0xf0, 0xa2, 0x8e, 0x51, 0x8a, 0xb6, 0x52, 0xec, 0xd4, 0x60, 0x29, 0xea, 0xc1, 0xce, |
| 0x53, 0xbc, 0xd2, 0x6c, 0x2a, 0x0d, 0x54, 0x47, 0xb8, 0xd2, 0xe9, 0x7e, 0x37, 0x60, 0xd7, 0xfb, |
| 0xa3, 0x6b, 0x34, 0xd6, 0x5f, 0x23, 0x9a, 0x40, 0x53, 0xf0, 0x85, 0xd9, 0x50, 0xff, 0xd9, 0xe8, |
| 0x5f, 0x2e, 0xd8, 0x0a, 0xf8, 0x22, 0x28, 0xc3, 0xbb, 0xa7, 0xd0, 0x0c, 0xf8, 0xa2, 0x9c, 0x99, |
| 0x3d, 0x90, 0x79, 0x96, 0x30, 0x4c, 0x79, 0x91, 0x4a, 0xd5, 0x53, 0x2b, 0xd8, 0xa9, 0x40, 0xaf, |
| 0xc4, 0x4e, 0x6f, 0x01, 0xad, 0x9e, 0x33, 0x7e, 0xcc, 0x18, 0x3a, 0x81, 0x23, 0x6f, 0xe8, 0x86, |
| 0xa1, 0x7f, 0xe9, 0x7b, 0xee, 0xd8, 0xff, 0x7c, 0x8d, 0xc7, 0xb7, 0xa3, 0x01, 0xbe, 0xb9, 0x0e, |
| 0x47, 0x03, 0xcf, 0xbf, 0xf4, 0x07, 0x9f, 0x3a, 0xff, 0xa1, 0x97, 0x00, 0x57, 0x37, 0xc3, 0xb1, |
| 0xaf, 0x94, 0x1d, 0xa3, 0xae, 0x87, 0xee, 0xc5, 0x60, 0xd8, 0x69, 0x5c, 0x3c, 0xc2, 0x11, 0xe5, |
| 0xf3, 0xe7, 0x46, 0xbc, 0xd8, 0x5b, 0x3d, 0x7b, 0x54, 0xbe, 0x16, 0x5f, 0xdc, 0xca, 0x11, 0xf1, |
| 0x84, 0xa4, 0x91, 0xc5, 0x45, 0x64, 0x47, 0x2c, 0x55, 0x2f, 0x89, 0xad, 0x29, 0x92, 0xc5, 0xf9, |
| 0xda, 0x97, 0xeb, 0xa3, 0x2e, 0x27, 0x6d, 0xa5, 0xfe, 0xf0, 0x33, 0x00, 0x00, 0xff, 0xff, 0x22, |
| 0xd4, 0x8f, 0x68, 0xe6, 0x04, 0x00, 0x00, |
| } |
