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doc/next/6-stdlib/2-unique.md
### New unique package The new [unique](/pkg/unique) package provides facilities for canonicalizing values (like "interning" or "hash-consing"). Any value of comparable type may be canonicalized with the new `Make[T]` function, which produces a reference to a canonical copy of the value in the form of a `Handle[T]`. Two `Handle[T]` are equal if and only if the values used to produce the handles are equal, allowing programs to deduplicate values and reduce
Plain Text - Registered: Tue May 07 11:14:38 GMT 2024 - Last Modified: Wed Apr 24 13:37:36 GMT 2024 - 579 bytes - Viewed (0) -
schema/index_test.go
FieldF2 string `gorm:"uniqueIndex:uniq_field_f1_f2;"` FieldG string `gorm:"unique;uniqueIndex"` // unique and uniqueIndex FieldH1 string `gorm:"unique;uniqueIndex:uniq_field_h1_h2"` // unique and mul uniqueIndex FieldH2 string `gorm:"uniqueIndex:uniq_field_h1_h2"` // unique and mul uniqueIndex } indexSchema, err := schema.Parse(&IndexTest{}, &sync.Map{}, schema.NamingStrategy{})
Go - Registered: Sun May 05 09:35:13 GMT 2024 - Last Modified: Sun Feb 04 07:49:19 GMT 2024 - 8K bytes - Viewed (0) -
tensorflow/c/checkpoint_reader.h
BuildV2VarMaps(); // Invariant: exactly one of "reader_" and "v2_reader_" is non-null. std::unique_ptr<TensorSliceReader> reader_; std::unique_ptr<BundleReader> v2_reader_; std::unique_ptr<TensorSliceReader::VarToShapeMap> var_to_shape_map_; std::unique_ptr<TensorSliceReader::VarToDataTypeMap> var_to_data_type_map_; CheckpointReader(const CheckpointReader&) = delete;
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api/next/62483.txt
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schema/constraint_test.go
Name: "uni_user_uniques_name1", Field: &schema.Field{Name: "Name1", Unique: true}, }, } for k, result := range results { v, ok := constraints[k] if !ok { t.Errorf("Failed to found unique constraint %v from parsed constraints %+v", k, constraints) } tests.AssertObjEqual(t, result, v, "Name") tests.AssertObjEqual(t, result.Field, v.Field, "Name", "Unique", "UniqueIndex") }
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migrator.go
Nullable() (nullable bool, ok bool) Unique() (unique bool, ok bool) ScanType() reflect.Type Comment() (value string, ok bool) DefaultValue() (value string, ok bool) } type Index interface { Table() string Name() string Columns() []string PrimaryKey() (isPrimaryKey bool, ok bool) Unique() (unique bool, ok bool) Option() string } // TableType table type interface
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tensorflow/c/experimental/filesystem/modular_filesystem.h
class ModularFileSystem final : public FileSystem { public: ModularFileSystem( std::unique_ptr<TF_Filesystem> filesystem, std::unique_ptr<const TF_FilesystemOps> filesystem_ops, std::unique_ptr<const TF_RandomAccessFileOps> random_access_file_ops, std::unique_ptr<const TF_WritableFileOps> writable_file_ops, std::unique_ptr<const TF_ReadOnlyMemoryRegionOps> read_only_memory_region_ops,
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Oct 12 08:49:52 GMT 2023 - 8.9K bytes - Viewed (0) -
schema/index.go
return } subName = composite } name = field.Schema.namer.IndexName( field.Schema.Table, subName) } if (k == "UNIQUEINDEX") || settings["UNIQUE"] != "" { settings["CLASS"] = "UNIQUE" } priority, err := strconv.Atoi(settings["PRIORITY"]) if err != nil { priority = 10 } indexes = append(indexes, Index{ Name: name,
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tensorflow/c/checkpoint_reader.cc
if (!status.ok()) out_tensor->reset(); } } if (!status.ok()) { tsl::Set_TF_Status_from_Status(out_status, status); } } std::pair<std::unique_ptr<TensorSliceReader::VarToShapeMap>, std::unique_ptr<TensorSliceReader::VarToDataTypeMap>> CheckpointReader::BuildV2VarMaps() { CHECK(v2_reader_ != nullptr); CHECK(v2_reader_->status().ok());
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schema/constraint.go
return "CONSTRAINT ? UNIQUE (?)", []interface{}{clause.Column{Name: uni.Name}, clause.Column{Name: uni.Field.DBName}} } // ParseUniqueConstraints parse schema unique constraints func (schema *Schema) ParseUniqueConstraints() map[string]UniqueConstraint { uniques := make(map[string]UniqueConstraint) for _, field := range schema.Fields { if field.Unique {
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