| 1 | // This file is MACHINE GENERATED! Do not edit. |
|---|---|
| 2 | |
| 3 | |
| 4 | #include "tensorflow/cc/ops/const_op.h" |
| 5 | #include "tensorflow/cc/ops/dataset_ops_internal.h" |
| 6 | |
| 7 | namespace tensorflow { |
| 8 | namespace ops { |
| 9 | namespace internal { |
| 10 | // NOTE: This namespace has internal TensorFlow details that |
| 11 | // are not part of TensorFlow's public API. |
| 12 | |
| 13 | AnonymousIteratorV2::AnonymousIteratorV2(const ::tensorflow::Scope& scope, |
| 14 | const DataTypeSlice& output_types, |
| 15 | const |
| 16 | gtl::ArraySlice<PartialTensorShape>& |
| 17 | output_shapes) { |
| 18 | if (!scope.ok()) return; |
| 19 | ::tensorflow::Node* ret; |
| 20 | const auto unique_name = scope.GetUniqueNameForOp("AnonymousIteratorV2"); |
| 21 | auto builder = ::tensorflow::NodeBuilder(unique_name, "AnonymousIteratorV2") |
| 22 | .Attr("output_types", output_types) |
| 23 | .Attr("output_shapes", output_shapes) |
| 24 | ; |
| 25 | scope.UpdateBuilder(&builder); |
| 26 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 27 | if (!scope.ok()) return; |
| 28 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 29 | this->operation = Operation(ret); |
| 30 | ::tensorflow::NameRangeMap _outputs_range; |
| 31 | ::tensorflow::Status _status_ = ::tensorflow::NameRangesForNode(*ret, ret->op_def(), nullptr, &_outputs_range); |
| 32 | if (!_status_.ok()) { |
| 33 | scope.UpdateStatus(_status_); |
| 34 | return; |
| 35 | } |
| 36 | |
| 37 | this->handle = Output(ret, _outputs_range["handle"].first); |
| 38 | this->deleter = Output(ret, _outputs_range["deleter"].first); |
| 39 | } |
| 40 | |
| 41 | AnonymousIteratorV3::AnonymousIteratorV3(const ::tensorflow::Scope& scope, |
| 42 | const DataTypeSlice& output_types, |
| 43 | const |
| 44 | gtl::ArraySlice<PartialTensorShape>& |
| 45 | output_shapes) { |
| 46 | if (!scope.ok()) return; |
| 47 | ::tensorflow::Node* ret; |
| 48 | const auto unique_name = scope.GetUniqueNameForOp("AnonymousIteratorV3"); |
| 49 | auto builder = ::tensorflow::NodeBuilder(unique_name, "AnonymousIteratorV3") |
| 50 | .Attr("output_types", output_types) |
| 51 | .Attr("output_shapes", output_shapes) |
| 52 | ; |
| 53 | scope.UpdateBuilder(&builder); |
| 54 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 55 | if (!scope.ok()) return; |
| 56 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 57 | this->operation = Operation(ret); |
| 58 | this->handle = Output(ret, 0); |
| 59 | } |
| 60 | |
| 61 | AnonymousMemoryCache::AnonymousMemoryCache(const ::tensorflow::Scope& scope) { |
| 62 | if (!scope.ok()) return; |
| 63 | ::tensorflow::Node* ret; |
| 64 | const auto unique_name = scope.GetUniqueNameForOp("AnonymousMemoryCache"); |
| 65 | auto builder = ::tensorflow::NodeBuilder(unique_name, "AnonymousMemoryCache") |
| 66 | ; |
| 67 | scope.UpdateBuilder(&builder); |
| 68 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 69 | if (!scope.ok()) return; |
| 70 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 71 | this->operation = Operation(ret); |
| 72 | ::tensorflow::NameRangeMap _outputs_range; |
| 73 | ::tensorflow::Status _status_ = ::tensorflow::NameRangesForNode(*ret, ret->op_def(), nullptr, &_outputs_range); |
| 74 | if (!_status_.ok()) { |
| 75 | scope.UpdateStatus(_status_); |
| 76 | return; |
| 77 | } |
| 78 | |
| 79 | this->handle = Output(ret, _outputs_range["handle"].first); |
| 80 | this->deleter = Output(ret, _outputs_range["deleter"].first); |
| 81 | } |
| 82 | |
| 83 | AnonymousMultiDeviceIterator::AnonymousMultiDeviceIterator(const |
| 84 | ::tensorflow::Scope& |
| 85 | scope, const |
| 86 | gtl::ArraySlice<::tensorflow::tstring>& |
| 87 | devices, const |
| 88 | DataTypeSlice& |
| 89 | output_types, const |
| 90 | gtl::ArraySlice<PartialTensorShape>& |
| 91 | output_shapes) { |
| 92 | if (!scope.ok()) return; |
| 93 | ::tensorflow::Node* ret; |
| 94 | const auto unique_name = scope.GetUniqueNameForOp("AnonymousMultiDeviceIterator"); |
| 95 | auto builder = ::tensorflow::NodeBuilder(unique_name, "AnonymousMultiDeviceIterator") |
| 96 | .Attr("devices", devices) |
| 97 | .Attr("output_types", output_types) |
| 98 | .Attr("output_shapes", output_shapes) |
| 99 | ; |
| 100 | scope.UpdateBuilder(&builder); |
| 101 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 102 | if (!scope.ok()) return; |
| 103 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 104 | this->operation = Operation(ret); |
| 105 | ::tensorflow::NameRangeMap _outputs_range; |
| 106 | ::tensorflow::Status _status_ = ::tensorflow::NameRangesForNode(*ret, ret->op_def(), nullptr, &_outputs_range); |
| 107 | if (!_status_.ok()) { |
| 108 | scope.UpdateStatus(_status_); |
| 109 | return; |
| 110 | } |
| 111 | |
| 112 | this->handle = Output(ret, _outputs_range["handle"].first); |
| 113 | this->deleter = Output(ret, _outputs_range["deleter"].first); |
| 114 | } |
| 115 | |
| 116 | AnonymousMultiDeviceIteratorV3::AnonymousMultiDeviceIteratorV3(const |
| 117 | ::tensorflow::Scope& |
| 118 | scope, const |
| 119 | gtl::ArraySlice<::tensorflow::tstring>& |
| 120 | devices, const |
| 121 | DataTypeSlice& |
| 122 | output_types, |
| 123 | const |
| 124 | gtl::ArraySlice<PartialTensorShape>& |
| 125 | output_shapes) { |
| 126 | if (!scope.ok()) return; |
| 127 | ::tensorflow::Node* ret; |
| 128 | const auto unique_name = scope.GetUniqueNameForOp("AnonymousMultiDeviceIteratorV3"); |
| 129 | auto builder = ::tensorflow::NodeBuilder(unique_name, "AnonymousMultiDeviceIteratorV3") |
| 130 | .Attr("devices", devices) |
| 131 | .Attr("output_types", output_types) |
| 132 | .Attr("output_shapes", output_shapes) |
| 133 | ; |
| 134 | scope.UpdateBuilder(&builder); |
| 135 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 136 | if (!scope.ok()) return; |
| 137 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 138 | this->operation = Operation(ret); |
| 139 | this->handle = Output(ret, 0); |
| 140 | } |
| 141 | |
| 142 | AnonymousRandomSeedGenerator::AnonymousRandomSeedGenerator(const |
| 143 | ::tensorflow::Scope& |
| 144 | scope, |
| 145 | ::tensorflow::Input |
| 146 | seed, |
| 147 | ::tensorflow::Input |
| 148 | seed2) { |
| 149 | if (!scope.ok()) return; |
| 150 | auto _seed = ::tensorflow::ops::AsNodeOut(scope, seed); |
| 151 | if (!scope.ok()) return; |
| 152 | auto _seed2 = ::tensorflow::ops::AsNodeOut(scope, seed2); |
| 153 | if (!scope.ok()) return; |
| 154 | ::tensorflow::Node* ret; |
| 155 | const auto unique_name = scope.GetUniqueNameForOp("AnonymousRandomSeedGenerator"); |
| 156 | auto builder = ::tensorflow::NodeBuilder(unique_name, "AnonymousRandomSeedGenerator") |
| 157 | .Input(_seed) |
| 158 | .Input(_seed2) |
| 159 | ; |
| 160 | scope.UpdateBuilder(&builder); |
| 161 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 162 | if (!scope.ok()) return; |
| 163 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 164 | this->operation = Operation(ret); |
| 165 | ::tensorflow::NameRangeMap _outputs_range; |
| 166 | ::tensorflow::Status _status_ = ::tensorflow::NameRangesForNode(*ret, ret->op_def(), nullptr, &_outputs_range); |
| 167 | if (!_status_.ok()) { |
| 168 | scope.UpdateStatus(_status_); |
| 169 | return; |
| 170 | } |
| 171 | |
| 172 | this->handle = Output(ret, _outputs_range["handle"].first); |
| 173 | this->deleter = Output(ret, _outputs_range["deleter"].first); |
| 174 | } |
| 175 | |
| 176 | AnonymousSeedGenerator::AnonymousSeedGenerator(const ::tensorflow::Scope& |
| 177 | scope, ::tensorflow::Input seed, |
| 178 | ::tensorflow::Input seed2, |
| 179 | ::tensorflow::Input reshuffle) { |
| 180 | if (!scope.ok()) return; |
| 181 | auto _seed = ::tensorflow::ops::AsNodeOut(scope, seed); |
| 182 | if (!scope.ok()) return; |
| 183 | auto _seed2 = ::tensorflow::ops::AsNodeOut(scope, seed2); |
| 184 | if (!scope.ok()) return; |
| 185 | auto _reshuffle = ::tensorflow::ops::AsNodeOut(scope, reshuffle); |
| 186 | if (!scope.ok()) return; |
| 187 | ::tensorflow::Node* ret; |
| 188 | const auto unique_name = scope.GetUniqueNameForOp("AnonymousSeedGenerator"); |
| 189 | auto builder = ::tensorflow::NodeBuilder(unique_name, "AnonymousSeedGenerator") |
| 190 | .Input(_seed) |
| 191 | .Input(_seed2) |
| 192 | .Input(_reshuffle) |
| 193 | ; |
| 194 | scope.UpdateBuilder(&builder); |
| 195 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 196 | if (!scope.ok()) return; |
| 197 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 198 | this->operation = Operation(ret); |
| 199 | ::tensorflow::NameRangeMap _outputs_range; |
| 200 | ::tensorflow::Status _status_ = ::tensorflow::NameRangesForNode(*ret, ret->op_def(), nullptr, &_outputs_range); |
| 201 | if (!_status_.ok()) { |
| 202 | scope.UpdateStatus(_status_); |
| 203 | return; |
| 204 | } |
| 205 | |
| 206 | this->handle = Output(ret, _outputs_range["handle"].first); |
| 207 | this->deleter = Output(ret, _outputs_range["deleter"].first); |
| 208 | } |
| 209 | |
| 210 | BatchDataset::BatchDataset(const ::tensorflow::Scope& scope, |
| 211 | ::tensorflow::Input input_dataset, |
| 212 | ::tensorflow::Input batch_size, const DataTypeSlice& |
| 213 | output_types, const |
| 214 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
| 215 | const BatchDataset::Attrs& attrs) { |
| 216 | if (!scope.ok()) return; |
| 217 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 218 | if (!scope.ok()) return; |
| 219 | auto _batch_size = ::tensorflow::ops::AsNodeOut(scope, batch_size); |
| 220 | if (!scope.ok()) return; |
| 221 | ::tensorflow::Node* ret; |
| 222 | const auto unique_name = scope.GetUniqueNameForOp("BatchDataset"); |
| 223 | auto builder = ::tensorflow::NodeBuilder(unique_name, "BatchDataset") |
| 224 | .Input(_input_dataset) |
| 225 | .Input(_batch_size) |
| 226 | .Attr("output_types", output_types) |
| 227 | .Attr("output_shapes", output_shapes) |
| 228 | .Attr("metadata", attrs.metadata_) |
| 229 | ; |
| 230 | scope.UpdateBuilder(&builder); |
| 231 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 232 | if (!scope.ok()) return; |
| 233 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 234 | this->operation = Operation(ret); |
| 235 | this->handle = Output(ret, 0); |
| 236 | } |
| 237 | |
| 238 | BatchDataset::BatchDataset(const ::tensorflow::Scope& scope, |
| 239 | ::tensorflow::Input input_dataset, |
| 240 | ::tensorflow::Input batch_size, const DataTypeSlice& |
| 241 | output_types, const |
| 242 | gtl::ArraySlice<PartialTensorShape>& output_shapes) |
| 243 | : BatchDataset(scope, input_dataset, batch_size, output_types, output_shapes, BatchDataset::Attrs()) {} |
| 244 | |
| 245 | BatchDatasetV2::BatchDatasetV2(const ::tensorflow::Scope& scope, |
| 246 | ::tensorflow::Input input_dataset, |
| 247 | ::tensorflow::Input batch_size, |
| 248 | ::tensorflow::Input drop_remainder, const |
| 249 | DataTypeSlice& output_types, const |
| 250 | gtl::ArraySlice<PartialTensorShape>& |
| 251 | output_shapes, const BatchDatasetV2::Attrs& |
| 252 | attrs) { |
| 253 | if (!scope.ok()) return; |
| 254 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 255 | if (!scope.ok()) return; |
| 256 | auto _batch_size = ::tensorflow::ops::AsNodeOut(scope, batch_size); |
| 257 | if (!scope.ok()) return; |
| 258 | auto _drop_remainder = ::tensorflow::ops::AsNodeOut(scope, drop_remainder); |
| 259 | if (!scope.ok()) return; |
| 260 | ::tensorflow::Node* ret; |
| 261 | const auto unique_name = scope.GetUniqueNameForOp("BatchDatasetV2"); |
| 262 | auto builder = ::tensorflow::NodeBuilder(unique_name, "BatchDatasetV2") |
| 263 | .Input(_input_dataset) |
| 264 | .Input(_batch_size) |
| 265 | .Input(_drop_remainder) |
| 266 | .Attr("parallel_copy", attrs.parallel_copy_) |
| 267 | .Attr("output_types", output_types) |
| 268 | .Attr("output_shapes", output_shapes) |
| 269 | .Attr("metadata", attrs.metadata_) |
| 270 | ; |
| 271 | scope.UpdateBuilder(&builder); |
| 272 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 273 | if (!scope.ok()) return; |
| 274 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 275 | this->operation = Operation(ret); |
| 276 | this->handle = Output(ret, 0); |
| 277 | } |
| 278 | |
| 279 | BatchDatasetV2::BatchDatasetV2(const ::tensorflow::Scope& scope, |
| 280 | ::tensorflow::Input input_dataset, |
| 281 | ::tensorflow::Input batch_size, |
| 282 | ::tensorflow::Input drop_remainder, const |
| 283 | DataTypeSlice& output_types, const |
| 284 | gtl::ArraySlice<PartialTensorShape>& |
| 285 | output_shapes) |
| 286 | : BatchDatasetV2(scope, input_dataset, batch_size, drop_remainder, output_types, output_shapes, BatchDatasetV2::Attrs()) {} |
| 287 | |
| 288 | CacheDataset::CacheDataset(const ::tensorflow::Scope& scope, |
| 289 | ::tensorflow::Input input_dataset, |
| 290 | ::tensorflow::Input filename, const DataTypeSlice& |
| 291 | output_types, const |
| 292 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
| 293 | const CacheDataset::Attrs& attrs) { |
| 294 | if (!scope.ok()) return; |
| 295 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 296 | if (!scope.ok()) return; |
| 297 | auto _filename = ::tensorflow::ops::AsNodeOut(scope, filename); |
| 298 | if (!scope.ok()) return; |
| 299 | ::tensorflow::Node* ret; |
| 300 | const auto unique_name = scope.GetUniqueNameForOp("CacheDataset"); |
| 301 | auto builder = ::tensorflow::NodeBuilder(unique_name, "CacheDataset") |
| 302 | .Input(_input_dataset) |
| 303 | .Input(_filename) |
| 304 | .Attr("output_types", output_types) |
| 305 | .Attr("output_shapes", output_shapes) |
| 306 | .Attr("metadata", attrs.metadata_) |
| 307 | ; |
| 308 | scope.UpdateBuilder(&builder); |
| 309 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 310 | if (!scope.ok()) return; |
| 311 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 312 | this->operation = Operation(ret); |
| 313 | this->handle = Output(ret, 0); |
| 314 | } |
| 315 | |
| 316 | CacheDataset::CacheDataset(const ::tensorflow::Scope& scope, |
| 317 | ::tensorflow::Input input_dataset, |
| 318 | ::tensorflow::Input filename, const DataTypeSlice& |
| 319 | output_types, const |
| 320 | gtl::ArraySlice<PartialTensorShape>& output_shapes) |
| 321 | : CacheDataset(scope, input_dataset, filename, output_types, output_shapes, CacheDataset::Attrs()) {} |
| 322 | |
| 323 | CacheDatasetV2::CacheDatasetV2(const ::tensorflow::Scope& scope, |
| 324 | ::tensorflow::Input input_dataset, |
| 325 | ::tensorflow::Input filename, |
| 326 | ::tensorflow::Input cache, const DataTypeSlice& |
| 327 | output_types, const |
| 328 | gtl::ArraySlice<PartialTensorShape>& |
| 329 | output_shapes, const CacheDatasetV2::Attrs& |
| 330 | attrs) { |
| 331 | if (!scope.ok()) return; |
| 332 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 333 | if (!scope.ok()) return; |
| 334 | auto _filename = ::tensorflow::ops::AsNodeOut(scope, filename); |
| 335 | if (!scope.ok()) return; |
| 336 | auto _cache = ::tensorflow::ops::AsNodeOut(scope, cache); |
| 337 | if (!scope.ok()) return; |
| 338 | ::tensorflow::Node* ret; |
| 339 | const auto unique_name = scope.GetUniqueNameForOp("CacheDatasetV2"); |
| 340 | auto builder = ::tensorflow::NodeBuilder(unique_name, "CacheDatasetV2") |
| 341 | .Input(_input_dataset) |
| 342 | .Input(_filename) |
| 343 | .Input(_cache) |
| 344 | .Attr("output_types", output_types) |
| 345 | .Attr("output_shapes", output_shapes) |
| 346 | .Attr("metadata", attrs.metadata_) |
| 347 | ; |
| 348 | scope.UpdateBuilder(&builder); |
| 349 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 350 | if (!scope.ok()) return; |
| 351 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 352 | this->operation = Operation(ret); |
| 353 | this->handle = Output(ret, 0); |
| 354 | } |
| 355 | |
| 356 | CacheDatasetV2::CacheDatasetV2(const ::tensorflow::Scope& scope, |
| 357 | ::tensorflow::Input input_dataset, |
| 358 | ::tensorflow::Input filename, |
| 359 | ::tensorflow::Input cache, const DataTypeSlice& |
| 360 | output_types, const |
| 361 | gtl::ArraySlice<PartialTensorShape>& |
| 362 | output_shapes) |
| 363 | : CacheDatasetV2(scope, input_dataset, filename, cache, output_types, output_shapes, CacheDatasetV2::Attrs()) {} |
| 364 | |
| 365 | ConcatenateDataset::ConcatenateDataset(const ::tensorflow::Scope& scope, |
| 366 | ::tensorflow::Input input_dataset, |
| 367 | ::tensorflow::Input another_dataset, |
| 368 | const DataTypeSlice& output_types, const |
| 369 | gtl::ArraySlice<PartialTensorShape>& |
| 370 | output_shapes, const |
| 371 | ConcatenateDataset::Attrs& attrs) { |
| 372 | if (!scope.ok()) return; |
| 373 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 374 | if (!scope.ok()) return; |
| 375 | auto _another_dataset = ::tensorflow::ops::AsNodeOut(scope, another_dataset); |
| 376 | if (!scope.ok()) return; |
| 377 | ::tensorflow::Node* ret; |
| 378 | const auto unique_name = scope.GetUniqueNameForOp("ConcatenateDataset"); |
| 379 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ConcatenateDataset") |
| 380 | .Input(_input_dataset) |
| 381 | .Input(_another_dataset) |
| 382 | .Attr("output_types", output_types) |
| 383 | .Attr("output_shapes", output_shapes) |
| 384 | .Attr("metadata", attrs.metadata_) |
| 385 | ; |
| 386 | scope.UpdateBuilder(&builder); |
| 387 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 388 | if (!scope.ok()) return; |
| 389 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 390 | this->operation = Operation(ret); |
| 391 | this->handle = Output(ret, 0); |
| 392 | } |
| 393 | |
| 394 | ConcatenateDataset::ConcatenateDataset(const ::tensorflow::Scope& scope, |
| 395 | ::tensorflow::Input input_dataset, |
| 396 | ::tensorflow::Input another_dataset, |
| 397 | const DataTypeSlice& output_types, const |
| 398 | gtl::ArraySlice<PartialTensorShape>& |
| 399 | output_shapes) |
| 400 | : ConcatenateDataset(scope, input_dataset, another_dataset, output_types, output_shapes, ConcatenateDataset::Attrs()) {} |
| 401 | |
| 402 | DatasetCardinality::DatasetCardinality(const ::tensorflow::Scope& scope, |
| 403 | ::tensorflow::Input input_dataset) { |
| 404 | if (!scope.ok()) return; |
| 405 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 406 | if (!scope.ok()) return; |
| 407 | ::tensorflow::Node* ret; |
| 408 | const auto unique_name = scope.GetUniqueNameForOp("DatasetCardinality"); |
| 409 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DatasetCardinality") |
| 410 | .Input(_input_dataset) |
| 411 | ; |
| 412 | scope.UpdateBuilder(&builder); |
| 413 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 414 | if (!scope.ok()) return; |
| 415 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 416 | this->operation = Operation(ret); |
| 417 | this->cardinality = Output(ret, 0); |
| 418 | } |
| 419 | |
| 420 | DatasetToGraph::DatasetToGraph(const ::tensorflow::Scope& scope, |
| 421 | ::tensorflow::Input input_dataset, const |
| 422 | DatasetToGraph::Attrs& attrs) { |
| 423 | if (!scope.ok()) return; |
| 424 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 425 | if (!scope.ok()) return; |
| 426 | ::tensorflow::Node* ret; |
| 427 | const auto unique_name = scope.GetUniqueNameForOp("DatasetToGraph"); |
| 428 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DatasetToGraph") |
| 429 | .Input(_input_dataset) |
| 430 | .Attr("stateful_whitelist", attrs.stateful_whitelist_) |
| 431 | .Attr("allow_stateful", attrs.allow_stateful_) |
| 432 | .Attr("strip_device_assignment", attrs.strip_device_assignment_) |
| 433 | ; |
| 434 | scope.UpdateBuilder(&builder); |
| 435 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 436 | if (!scope.ok()) return; |
| 437 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 438 | this->operation = Operation(ret); |
| 439 | this->graph = Output(ret, 0); |
| 440 | } |
| 441 | |
| 442 | DatasetToGraph::DatasetToGraph(const ::tensorflow::Scope& scope, |
| 443 | ::tensorflow::Input input_dataset) |
| 444 | : DatasetToGraph(scope, input_dataset, DatasetToGraph::Attrs()) {} |
| 445 | |
| 446 | DatasetToGraphV2::DatasetToGraphV2(const ::tensorflow::Scope& scope, |
| 447 | ::tensorflow::Input input_dataset, const |
| 448 | DatasetToGraphV2::Attrs& attrs) { |
| 449 | if (!scope.ok()) return; |
| 450 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 451 | if (!scope.ok()) return; |
| 452 | ::tensorflow::Node* ret; |
| 453 | const auto unique_name = scope.GetUniqueNameForOp("DatasetToGraphV2"); |
| 454 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DatasetToGraphV2") |
| 455 | .Input(_input_dataset) |
| 456 | .Attr("external_state_policy", attrs.external_state_policy_) |
| 457 | .Attr("strip_device_assignment", attrs.strip_device_assignment_) |
| 458 | ; |
| 459 | scope.UpdateBuilder(&builder); |
| 460 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 461 | if (!scope.ok()) return; |
| 462 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 463 | this->operation = Operation(ret); |
| 464 | this->graph = Output(ret, 0); |
| 465 | } |
| 466 | |
| 467 | DatasetToGraphV2::DatasetToGraphV2(const ::tensorflow::Scope& scope, |
| 468 | ::tensorflow::Input input_dataset) |
| 469 | : DatasetToGraphV2(scope, input_dataset, DatasetToGraphV2::Attrs()) {} |
| 470 | |
| 471 | DatasetToSingleElement::DatasetToSingleElement(const ::tensorflow::Scope& |
| 472 | scope, ::tensorflow::Input |
| 473 | dataset, const DataTypeSlice& |
| 474 | output_types, const |
| 475 | gtl::ArraySlice<PartialTensorShape>& |
| 476 | output_shapes, const |
| 477 | DatasetToSingleElement::Attrs& |
| 478 | attrs) { |
| 479 | if (!scope.ok()) return; |
| 480 | auto _dataset = ::tensorflow::ops::AsNodeOut(scope, dataset); |
| 481 | if (!scope.ok()) return; |
| 482 | ::tensorflow::Node* ret; |
| 483 | const auto unique_name = scope.GetUniqueNameForOp("DatasetToSingleElement"); |
| 484 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DatasetToSingleElement") |
| 485 | .Input(_dataset) |
| 486 | .Attr("output_types", output_types) |
| 487 | .Attr("output_shapes", output_shapes) |
| 488 | .Attr("metadata", attrs.metadata_) |
| 489 | ; |
| 490 | scope.UpdateBuilder(&builder); |
| 491 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 492 | if (!scope.ok()) return; |
| 493 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 494 | this->operation = Operation(ret); |
| 495 | for (int32 i = 0; i < ret->num_outputs(); ++i) |
| 496 | this->components.push_back(Output(ret, i)); |
| 497 | } |
| 498 | |
| 499 | DatasetToSingleElement::DatasetToSingleElement(const ::tensorflow::Scope& |
| 500 | scope, ::tensorflow::Input |
| 501 | dataset, const DataTypeSlice& |
| 502 | output_types, const |
| 503 | gtl::ArraySlice<PartialTensorShape>& |
| 504 | output_shapes) |
| 505 | : DatasetToSingleElement(scope, dataset, output_types, output_shapes, DatasetToSingleElement::Attrs()) {} |
| 506 | |
| 507 | DeleteIterator::DeleteIterator(const ::tensorflow::Scope& scope, |
| 508 | ::tensorflow::Input handle, ::tensorflow::Input |
| 509 | deleter) { |
| 510 | if (!scope.ok()) return; |
| 511 | auto _handle = ::tensorflow::ops::AsNodeOut(scope, handle); |
| 512 | if (!scope.ok()) return; |
| 513 | auto _deleter = ::tensorflow::ops::AsNodeOut(scope, deleter); |
| 514 | if (!scope.ok()) return; |
| 515 | ::tensorflow::Node* ret; |
| 516 | const auto unique_name = scope.GetUniqueNameForOp("DeleteIterator"); |
| 517 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DeleteIterator") |
| 518 | .Input(_handle) |
| 519 | .Input(_deleter) |
| 520 | ; |
| 521 | scope.UpdateBuilder(&builder); |
| 522 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 523 | if (!scope.ok()) return; |
| 524 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 525 | this->operation = Operation(ret); |
| 526 | return; |
| 527 | } |
| 528 | |
| 529 | DeleteMemoryCache::DeleteMemoryCache(const ::tensorflow::Scope& scope, |
| 530 | ::tensorflow::Input handle, |
| 531 | ::tensorflow::Input deleter) { |
| 532 | if (!scope.ok()) return; |
| 533 | auto _handle = ::tensorflow::ops::AsNodeOut(scope, handle); |
| 534 | if (!scope.ok()) return; |
| 535 | auto _deleter = ::tensorflow::ops::AsNodeOut(scope, deleter); |
| 536 | if (!scope.ok()) return; |
| 537 | ::tensorflow::Node* ret; |
| 538 | const auto unique_name = scope.GetUniqueNameForOp("DeleteMemoryCache"); |
| 539 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DeleteMemoryCache") |
| 540 | .Input(_handle) |
| 541 | .Input(_deleter) |
| 542 | ; |
| 543 | scope.UpdateBuilder(&builder); |
| 544 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 545 | if (!scope.ok()) return; |
| 546 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 547 | this->operation = Operation(ret); |
| 548 | return; |
| 549 | } |
| 550 | |
| 551 | DeleteMultiDeviceIterator::DeleteMultiDeviceIterator(const ::tensorflow::Scope& |
| 552 | scope, ::tensorflow::Input |
| 553 | multi_device_iterator, |
| 554 | ::tensorflow::InputList |
| 555 | iterators, |
| 556 | ::tensorflow::Input |
| 557 | deleter) { |
| 558 | if (!scope.ok()) return; |
| 559 | auto _multi_device_iterator = ::tensorflow::ops::AsNodeOut(scope, multi_device_iterator); |
| 560 | if (!scope.ok()) return; |
| 561 | auto _iterators = ::tensorflow::ops::AsNodeOutList(scope, iterators); |
| 562 | if (!scope.ok()) return; |
| 563 | auto _deleter = ::tensorflow::ops::AsNodeOut(scope, deleter); |
| 564 | if (!scope.ok()) return; |
| 565 | ::tensorflow::Node* ret; |
| 566 | const auto unique_name = scope.GetUniqueNameForOp("DeleteMultiDeviceIterator"); |
| 567 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DeleteMultiDeviceIterator") |
| 568 | .Input(_multi_device_iterator) |
| 569 | .Input(_iterators) |
| 570 | .Input(_deleter) |
| 571 | ; |
| 572 | scope.UpdateBuilder(&builder); |
| 573 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 574 | if (!scope.ok()) return; |
| 575 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 576 | this->operation = Operation(ret); |
| 577 | return; |
| 578 | } |
| 579 | |
| 580 | DeleteRandomSeedGenerator::DeleteRandomSeedGenerator(const ::tensorflow::Scope& |
| 581 | scope, ::tensorflow::Input |
| 582 | handle, |
| 583 | ::tensorflow::Input |
| 584 | deleter) { |
| 585 | if (!scope.ok()) return; |
| 586 | auto _handle = ::tensorflow::ops::AsNodeOut(scope, handle); |
| 587 | if (!scope.ok()) return; |
| 588 | auto _deleter = ::tensorflow::ops::AsNodeOut(scope, deleter); |
| 589 | if (!scope.ok()) return; |
| 590 | ::tensorflow::Node* ret; |
| 591 | const auto unique_name = scope.GetUniqueNameForOp("DeleteRandomSeedGenerator"); |
| 592 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DeleteRandomSeedGenerator") |
| 593 | .Input(_handle) |
| 594 | .Input(_deleter) |
| 595 | ; |
| 596 | scope.UpdateBuilder(&builder); |
| 597 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 598 | if (!scope.ok()) return; |
| 599 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 600 | this->operation = Operation(ret); |
| 601 | return; |
| 602 | } |
| 603 | |
| 604 | DeleteSeedGenerator::DeleteSeedGenerator(const ::tensorflow::Scope& scope, |
| 605 | ::tensorflow::Input handle, |
| 606 | ::tensorflow::Input deleter) { |
| 607 | if (!scope.ok()) return; |
| 608 | auto _handle = ::tensorflow::ops::AsNodeOut(scope, handle); |
| 609 | if (!scope.ok()) return; |
| 610 | auto _deleter = ::tensorflow::ops::AsNodeOut(scope, deleter); |
| 611 | if (!scope.ok()) return; |
| 612 | ::tensorflow::Node* ret; |
| 613 | const auto unique_name = scope.GetUniqueNameForOp("DeleteSeedGenerator"); |
| 614 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DeleteSeedGenerator") |
| 615 | .Input(_handle) |
| 616 | .Input(_deleter) |
| 617 | ; |
| 618 | scope.UpdateBuilder(&builder); |
| 619 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 620 | if (!scope.ok()) return; |
| 621 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 622 | this->operation = Operation(ret); |
| 623 | return; |
| 624 | } |
| 625 | |
| 626 | DummyMemoryCache::DummyMemoryCache(const ::tensorflow::Scope& scope) { |
| 627 | if (!scope.ok()) return; |
| 628 | ::tensorflow::Node* ret; |
| 629 | const auto unique_name = scope.GetUniqueNameForOp("DummyMemoryCache"); |
| 630 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DummyMemoryCache") |
| 631 | ; |
| 632 | scope.UpdateBuilder(&builder); |
| 633 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 634 | if (!scope.ok()) return; |
| 635 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 636 | this->operation = Operation(ret); |
| 637 | this->handle = Output(ret, 0); |
| 638 | } |
| 639 | |
| 640 | DummySeedGenerator::DummySeedGenerator(const ::tensorflow::Scope& scope) { |
| 641 | if (!scope.ok()) return; |
| 642 | ::tensorflow::Node* ret; |
| 643 | const auto unique_name = scope.GetUniqueNameForOp("DummySeedGenerator"); |
| 644 | auto builder = ::tensorflow::NodeBuilder(unique_name, "DummySeedGenerator") |
| 645 | ; |
| 646 | scope.UpdateBuilder(&builder); |
| 647 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 648 | if (!scope.ok()) return; |
| 649 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 650 | this->operation = Operation(ret); |
| 651 | this->handle = Output(ret, 0); |
| 652 | } |
| 653 | |
| 654 | FilterByLastComponentDataset::FilterByLastComponentDataset(const |
| 655 | ::tensorflow::Scope& |
| 656 | scope, |
| 657 | ::tensorflow::Input |
| 658 | input_dataset, const |
| 659 | DataTypeSlice& |
| 660 | output_types, const |
| 661 | gtl::ArraySlice<PartialTensorShape>& |
| 662 | output_shapes) { |
| 663 | if (!scope.ok()) return; |
| 664 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 665 | if (!scope.ok()) return; |
| 666 | ::tensorflow::Node* ret; |
| 667 | const auto unique_name = scope.GetUniqueNameForOp("FilterByLastComponentDataset"); |
| 668 | auto builder = ::tensorflow::NodeBuilder(unique_name, "FilterByLastComponentDataset") |
| 669 | .Input(_input_dataset) |
| 670 | .Attr("output_types", output_types) |
| 671 | .Attr("output_shapes", output_shapes) |
| 672 | ; |
| 673 | scope.UpdateBuilder(&builder); |
| 674 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 675 | if (!scope.ok()) return; |
| 676 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 677 | this->operation = Operation(ret); |
| 678 | this->output = Output(ret, 0); |
| 679 | } |
| 680 | |
| 681 | FilterDataset::FilterDataset(const ::tensorflow::Scope& scope, |
| 682 | ::tensorflow::Input input_dataset, |
| 683 | ::tensorflow::InputList other_arguments, const |
| 684 | NameAttrList& predicate, const DataTypeSlice& |
| 685 | output_types, const |
| 686 | gtl::ArraySlice<PartialTensorShape>& |
| 687 | output_shapes, const FilterDataset::Attrs& attrs) { |
| 688 | if (!scope.ok()) return; |
| 689 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 690 | if (!scope.ok()) return; |
| 691 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 692 | if (!scope.ok()) return; |
| 693 | ::tensorflow::Node* ret; |
| 694 | const auto unique_name = scope.GetUniqueNameForOp("FilterDataset"); |
| 695 | auto builder = ::tensorflow::NodeBuilder(unique_name, "FilterDataset") |
| 696 | .Input(_input_dataset) |
| 697 | .Input(_other_arguments) |
| 698 | .Attr("predicate", predicate) |
| 699 | .Attr("output_types", output_types) |
| 700 | .Attr("output_shapes", output_shapes) |
| 701 | .Attr("metadata", attrs.metadata_) |
| 702 | ; |
| 703 | scope.UpdateBuilder(&builder); |
| 704 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 705 | if (!scope.ok()) return; |
| 706 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 707 | this->operation = Operation(ret); |
| 708 | this->handle = Output(ret, 0); |
| 709 | } |
| 710 | |
| 711 | FilterDataset::FilterDataset(const ::tensorflow::Scope& scope, |
| 712 | ::tensorflow::Input input_dataset, |
| 713 | ::tensorflow::InputList other_arguments, const |
| 714 | NameAttrList& predicate, const DataTypeSlice& |
| 715 | output_types, const |
| 716 | gtl::ArraySlice<PartialTensorShape>& |
| 717 | output_shapes) |
| 718 | : FilterDataset(scope, input_dataset, other_arguments, predicate, output_types, output_shapes, FilterDataset::Attrs()) {} |
| 719 | |
| 720 | FinalizeDataset::FinalizeDataset(const ::tensorflow::Scope& scope, |
| 721 | ::tensorflow::Input input_dataset, const |
| 722 | DataTypeSlice& output_types, const |
| 723 | gtl::ArraySlice<PartialTensorShape>& |
| 724 | output_shapes, const FinalizeDataset::Attrs& |
| 725 | attrs) { |
| 726 | if (!scope.ok()) return; |
| 727 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 728 | if (!scope.ok()) return; |
| 729 | ::tensorflow::Node* ret; |
| 730 | const auto unique_name = scope.GetUniqueNameForOp("FinalizeDataset"); |
| 731 | auto builder = ::tensorflow::NodeBuilder(unique_name, "FinalizeDataset") |
| 732 | .Input(_input_dataset) |
| 733 | .Attr("has_captured_ref", attrs.has_captured_ref_) |
| 734 | .Attr("output_types", output_types) |
| 735 | .Attr("output_shapes", output_shapes) |
| 736 | ; |
| 737 | scope.UpdateBuilder(&builder); |
| 738 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 739 | if (!scope.ok()) return; |
| 740 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 741 | this->operation = Operation(ret); |
| 742 | this->handle = Output(ret, 0); |
| 743 | } |
| 744 | |
| 745 | FinalizeDataset::FinalizeDataset(const ::tensorflow::Scope& scope, |
| 746 | ::tensorflow::Input input_dataset, const |
| 747 | DataTypeSlice& output_types, const |
| 748 | gtl::ArraySlice<PartialTensorShape>& |
| 749 | output_shapes) |
| 750 | : FinalizeDataset(scope, input_dataset, output_types, output_shapes, FinalizeDataset::Attrs()) {} |
| 751 | |
| 752 | FixedLengthRecordDataset::FixedLengthRecordDataset(const ::tensorflow::Scope& |
| 753 | scope, ::tensorflow::Input |
| 754 | filenames, |
| 755 | ::tensorflow::Input |
| 756 | header_bytes, |
| 757 | ::tensorflow::Input |
| 758 | record_bytes, |
| 759 | ::tensorflow::Input |
| 760 | footer_bytes, |
| 761 | ::tensorflow::Input |
| 762 | buffer_size, const |
| 763 | FixedLengthRecordDataset::Attrs& |
| 764 | attrs) { |
| 765 | if (!scope.ok()) return; |
| 766 | auto _filenames = ::tensorflow::ops::AsNodeOut(scope, filenames); |
| 767 | if (!scope.ok()) return; |
| 768 | auto _header_bytes = ::tensorflow::ops::AsNodeOut(scope, header_bytes); |
| 769 | if (!scope.ok()) return; |
| 770 | auto _record_bytes = ::tensorflow::ops::AsNodeOut(scope, record_bytes); |
| 771 | if (!scope.ok()) return; |
| 772 | auto _footer_bytes = ::tensorflow::ops::AsNodeOut(scope, footer_bytes); |
| 773 | if (!scope.ok()) return; |
| 774 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 775 | if (!scope.ok()) return; |
| 776 | ::tensorflow::Node* ret; |
| 777 | const auto unique_name = scope.GetUniqueNameForOp("FixedLengthRecordDataset"); |
| 778 | auto builder = ::tensorflow::NodeBuilder(unique_name, "FixedLengthRecordDataset") |
| 779 | .Input(_filenames) |
| 780 | .Input(_header_bytes) |
| 781 | .Input(_record_bytes) |
| 782 | .Input(_footer_bytes) |
| 783 | .Input(_buffer_size) |
| 784 | .Attr("metadata", attrs.metadata_) |
| 785 | ; |
| 786 | scope.UpdateBuilder(&builder); |
| 787 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 788 | if (!scope.ok()) return; |
| 789 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 790 | this->operation = Operation(ret); |
| 791 | this->handle = Output(ret, 0); |
| 792 | } |
| 793 | |
| 794 | FixedLengthRecordDataset::FixedLengthRecordDataset(const ::tensorflow::Scope& |
| 795 | scope, ::tensorflow::Input |
| 796 | filenames, |
| 797 | ::tensorflow::Input |
| 798 | header_bytes, |
| 799 | ::tensorflow::Input |
| 800 | record_bytes, |
| 801 | ::tensorflow::Input |
| 802 | footer_bytes, |
| 803 | ::tensorflow::Input |
| 804 | buffer_size) |
| 805 | : FixedLengthRecordDataset(scope, filenames, header_bytes, record_bytes, footer_bytes, buffer_size, FixedLengthRecordDataset::Attrs()) {} |
| 806 | |
| 807 | FixedLengthRecordDatasetV2::FixedLengthRecordDatasetV2(const |
| 808 | ::tensorflow::Scope& |
| 809 | scope, |
| 810 | ::tensorflow::Input |
| 811 | filenames, |
| 812 | ::tensorflow::Input |
| 813 | header_bytes, |
| 814 | ::tensorflow::Input |
| 815 | record_bytes, |
| 816 | ::tensorflow::Input |
| 817 | footer_bytes, |
| 818 | ::tensorflow::Input |
| 819 | buffer_size, |
| 820 | ::tensorflow::Input |
| 821 | compression_type, const |
| 822 | FixedLengthRecordDatasetV2::Attrs& |
| 823 | attrs) { |
| 824 | if (!scope.ok()) return; |
| 825 | auto _filenames = ::tensorflow::ops::AsNodeOut(scope, filenames); |
| 826 | if (!scope.ok()) return; |
| 827 | auto _header_bytes = ::tensorflow::ops::AsNodeOut(scope, header_bytes); |
| 828 | if (!scope.ok()) return; |
| 829 | auto _record_bytes = ::tensorflow::ops::AsNodeOut(scope, record_bytes); |
| 830 | if (!scope.ok()) return; |
| 831 | auto _footer_bytes = ::tensorflow::ops::AsNodeOut(scope, footer_bytes); |
| 832 | if (!scope.ok()) return; |
| 833 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 834 | if (!scope.ok()) return; |
| 835 | auto _compression_type = ::tensorflow::ops::AsNodeOut(scope, compression_type); |
| 836 | if (!scope.ok()) return; |
| 837 | ::tensorflow::Node* ret; |
| 838 | const auto unique_name = scope.GetUniqueNameForOp("FixedLengthRecordDatasetV2"); |
| 839 | auto builder = ::tensorflow::NodeBuilder(unique_name, "FixedLengthRecordDatasetV2") |
| 840 | .Input(_filenames) |
| 841 | .Input(_header_bytes) |
| 842 | .Input(_record_bytes) |
| 843 | .Input(_footer_bytes) |
| 844 | .Input(_buffer_size) |
| 845 | .Input(_compression_type) |
| 846 | .Attr("metadata", attrs.metadata_) |
| 847 | ; |
| 848 | scope.UpdateBuilder(&builder); |
| 849 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 850 | if (!scope.ok()) return; |
| 851 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 852 | this->operation = Operation(ret); |
| 853 | this->handle = Output(ret, 0); |
| 854 | } |
| 855 | |
| 856 | FixedLengthRecordDatasetV2::FixedLengthRecordDatasetV2(const |
| 857 | ::tensorflow::Scope& |
| 858 | scope, |
| 859 | ::tensorflow::Input |
| 860 | filenames, |
| 861 | ::tensorflow::Input |
| 862 | header_bytes, |
| 863 | ::tensorflow::Input |
| 864 | record_bytes, |
| 865 | ::tensorflow::Input |
| 866 | footer_bytes, |
| 867 | ::tensorflow::Input |
| 868 | buffer_size, |
| 869 | ::tensorflow::Input |
| 870 | compression_type) |
| 871 | : FixedLengthRecordDatasetV2(scope, filenames, header_bytes, record_bytes, footer_bytes, buffer_size, compression_type, FixedLengthRecordDatasetV2::Attrs()) {} |
| 872 | |
| 873 | FlatMapDataset::FlatMapDataset(const ::tensorflow::Scope& scope, |
| 874 | ::tensorflow::Input input_dataset, |
| 875 | ::tensorflow::InputList other_arguments, const |
| 876 | NameAttrList& f, const DataTypeSlice& |
| 877 | output_types, const |
| 878 | gtl::ArraySlice<PartialTensorShape>& |
| 879 | output_shapes, const FlatMapDataset::Attrs& |
| 880 | attrs) { |
| 881 | if (!scope.ok()) return; |
| 882 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 883 | if (!scope.ok()) return; |
| 884 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 885 | if (!scope.ok()) return; |
| 886 | ::tensorflow::Node* ret; |
| 887 | const auto unique_name = scope.GetUniqueNameForOp("FlatMapDataset"); |
| 888 | auto builder = ::tensorflow::NodeBuilder(unique_name, "FlatMapDataset") |
| 889 | .Input(_input_dataset) |
| 890 | .Input(_other_arguments) |
| 891 | .Attr("f", f) |
| 892 | .Attr("output_types", output_types) |
| 893 | .Attr("output_shapes", output_shapes) |
| 894 | .Attr("metadata", attrs.metadata_) |
| 895 | ; |
| 896 | scope.UpdateBuilder(&builder); |
| 897 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 898 | if (!scope.ok()) return; |
| 899 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 900 | this->operation = Operation(ret); |
| 901 | this->handle = Output(ret, 0); |
| 902 | } |
| 903 | |
| 904 | FlatMapDataset::FlatMapDataset(const ::tensorflow::Scope& scope, |
| 905 | ::tensorflow::Input input_dataset, |
| 906 | ::tensorflow::InputList other_arguments, const |
| 907 | NameAttrList& f, const DataTypeSlice& |
| 908 | output_types, const |
| 909 | gtl::ArraySlice<PartialTensorShape>& |
| 910 | output_shapes) |
| 911 | : FlatMapDataset(scope, input_dataset, other_arguments, f, output_types, output_shapes, FlatMapDataset::Attrs()) {} |
| 912 | |
| 913 | GeneratorDataset::GeneratorDataset(const ::tensorflow::Scope& scope, |
| 914 | ::tensorflow::InputList |
| 915 | init_func_other_args, |
| 916 | ::tensorflow::InputList |
| 917 | next_func_other_args, |
| 918 | ::tensorflow::InputList |
| 919 | finalize_func_other_args, const |
| 920 | NameAttrList& init_func, const NameAttrList& |
| 921 | next_func, const NameAttrList& |
| 922 | finalize_func, const DataTypeSlice& |
| 923 | output_types, const |
| 924 | gtl::ArraySlice<PartialTensorShape>& |
| 925 | output_shapes, const |
| 926 | GeneratorDataset::Attrs& attrs) { |
| 927 | if (!scope.ok()) return; |
| 928 | auto _init_func_other_args = ::tensorflow::ops::AsNodeOutList(scope, init_func_other_args); |
| 929 | if (!scope.ok()) return; |
| 930 | auto _next_func_other_args = ::tensorflow::ops::AsNodeOutList(scope, next_func_other_args); |
| 931 | if (!scope.ok()) return; |
| 932 | auto _finalize_func_other_args = ::tensorflow::ops::AsNodeOutList(scope, finalize_func_other_args); |
| 933 | if (!scope.ok()) return; |
| 934 | ::tensorflow::Node* ret; |
| 935 | const auto unique_name = scope.GetUniqueNameForOp("GeneratorDataset"); |
| 936 | auto builder = ::tensorflow::NodeBuilder(unique_name, "GeneratorDataset") |
| 937 | .Input(_init_func_other_args) |
| 938 | .Input(_next_func_other_args) |
| 939 | .Input(_finalize_func_other_args) |
| 940 | .Attr("init_func", init_func) |
| 941 | .Attr("next_func", next_func) |
| 942 | .Attr("finalize_func", finalize_func) |
| 943 | .Attr("output_types", output_types) |
| 944 | .Attr("output_shapes", output_shapes) |
| 945 | .Attr("metadata", attrs.metadata_) |
| 946 | ; |
| 947 | scope.UpdateBuilder(&builder); |
| 948 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 949 | if (!scope.ok()) return; |
| 950 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 951 | this->operation = Operation(ret); |
| 952 | this->handle = Output(ret, 0); |
| 953 | } |
| 954 | |
| 955 | GeneratorDataset::GeneratorDataset(const ::tensorflow::Scope& scope, |
| 956 | ::tensorflow::InputList |
| 957 | init_func_other_args, |
| 958 | ::tensorflow::InputList |
| 959 | next_func_other_args, |
| 960 | ::tensorflow::InputList |
| 961 | finalize_func_other_args, const |
| 962 | NameAttrList& init_func, const NameAttrList& |
| 963 | next_func, const NameAttrList& |
| 964 | finalize_func, const DataTypeSlice& |
| 965 | output_types, const |
| 966 | gtl::ArraySlice<PartialTensorShape>& |
| 967 | output_shapes) |
| 968 | : GeneratorDataset(scope, init_func_other_args, next_func_other_args, finalize_func_other_args, init_func, next_func, finalize_func, output_types, output_shapes, GeneratorDataset::Attrs()) {} |
| 969 | |
| 970 | GetOptions::GetOptions(const ::tensorflow::Scope& scope, ::tensorflow::Input |
| 971 | input_dataset) { |
| 972 | if (!scope.ok()) return; |
| 973 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 974 | if (!scope.ok()) return; |
| 975 | ::tensorflow::Node* ret; |
| 976 | const auto unique_name = scope.GetUniqueNameForOp("GetOptions"); |
| 977 | auto builder = ::tensorflow::NodeBuilder(unique_name, "GetOptions") |
| 978 | .Input(_input_dataset) |
| 979 | ; |
| 980 | scope.UpdateBuilder(&builder); |
| 981 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 982 | if (!scope.ok()) return; |
| 983 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 984 | this->operation = Operation(ret); |
| 985 | this->serialized_options = Output(ret, 0); |
| 986 | } |
| 987 | |
| 988 | InterleaveDataset::InterleaveDataset(const ::tensorflow::Scope& scope, |
| 989 | ::tensorflow::Input input_dataset, |
| 990 | ::tensorflow::InputList other_arguments, |
| 991 | ::tensorflow::Input cycle_length, |
| 992 | ::tensorflow::Input block_length, const |
| 993 | NameAttrList& f, const DataTypeSlice& |
| 994 | output_types, const |
| 995 | gtl::ArraySlice<PartialTensorShape>& |
| 996 | output_shapes, const |
| 997 | InterleaveDataset::Attrs& attrs) { |
| 998 | if (!scope.ok()) return; |
| 999 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1000 | if (!scope.ok()) return; |
| 1001 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 1002 | if (!scope.ok()) return; |
| 1003 | auto _cycle_length = ::tensorflow::ops::AsNodeOut(scope, cycle_length); |
| 1004 | if (!scope.ok()) return; |
| 1005 | auto _block_length = ::tensorflow::ops::AsNodeOut(scope, block_length); |
| 1006 | if (!scope.ok()) return; |
| 1007 | ::tensorflow::Node* ret; |
| 1008 | const auto unique_name = scope.GetUniqueNameForOp("InterleaveDataset"); |
| 1009 | auto builder = ::tensorflow::NodeBuilder(unique_name, "InterleaveDataset") |
| 1010 | .Input(_input_dataset) |
| 1011 | .Input(_other_arguments) |
| 1012 | .Input(_cycle_length) |
| 1013 | .Input(_block_length) |
| 1014 | .Attr("f", f) |
| 1015 | .Attr("output_types", output_types) |
| 1016 | .Attr("output_shapes", output_shapes) |
| 1017 | .Attr("metadata", attrs.metadata_) |
| 1018 | ; |
| 1019 | scope.UpdateBuilder(&builder); |
| 1020 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1021 | if (!scope.ok()) return; |
| 1022 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1023 | this->operation = Operation(ret); |
| 1024 | this->handle = Output(ret, 0); |
| 1025 | } |
| 1026 | |
| 1027 | InterleaveDataset::InterleaveDataset(const ::tensorflow::Scope& scope, |
| 1028 | ::tensorflow::Input input_dataset, |
| 1029 | ::tensorflow::InputList other_arguments, |
| 1030 | ::tensorflow::Input cycle_length, |
| 1031 | ::tensorflow::Input block_length, const |
| 1032 | NameAttrList& f, const DataTypeSlice& |
| 1033 | output_types, const |
| 1034 | gtl::ArraySlice<PartialTensorShape>& |
| 1035 | output_shapes) |
| 1036 | : InterleaveDataset(scope, input_dataset, other_arguments, cycle_length, block_length, f, output_types, output_shapes, InterleaveDataset::Attrs()) {} |
| 1037 | |
| 1038 | IteratorFromStringHandleV2::IteratorFromStringHandleV2(const |
| 1039 | ::tensorflow::Scope& |
| 1040 | scope, |
| 1041 | ::tensorflow::Input |
| 1042 | string_handle, const |
| 1043 | IteratorFromStringHandleV2::Attrs& |
| 1044 | attrs) { |
| 1045 | if (!scope.ok()) return; |
| 1046 | auto _string_handle = ::tensorflow::ops::AsNodeOut(scope, string_handle); |
| 1047 | if (!scope.ok()) return; |
| 1048 | ::tensorflow::Node* ret; |
| 1049 | const auto unique_name = scope.GetUniqueNameForOp("IteratorFromStringHandleV2"); |
| 1050 | auto builder = ::tensorflow::NodeBuilder(unique_name, "IteratorFromStringHandleV2") |
| 1051 | .Input(_string_handle) |
| 1052 | .Attr("output_types", attrs.output_types_) |
| 1053 | .Attr("output_shapes", attrs.output_shapes_) |
| 1054 | ; |
| 1055 | scope.UpdateBuilder(&builder); |
| 1056 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1057 | if (!scope.ok()) return; |
| 1058 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1059 | this->operation = Operation(ret); |
| 1060 | this->resource_handle = Output(ret, 0); |
| 1061 | } |
| 1062 | |
| 1063 | IteratorFromStringHandleV2::IteratorFromStringHandleV2(const |
| 1064 | ::tensorflow::Scope& |
| 1065 | scope, |
| 1066 | ::tensorflow::Input |
| 1067 | string_handle) |
| 1068 | : IteratorFromStringHandleV2(scope, string_handle, IteratorFromStringHandleV2::Attrs()) {} |
| 1069 | |
| 1070 | IteratorV2::IteratorV2(const ::tensorflow::Scope& scope, StringPiece |
| 1071 | shared_name, StringPiece container, const DataTypeSlice& |
| 1072 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
| 1073 | output_shapes) { |
| 1074 | if (!scope.ok()) return; |
| 1075 | ::tensorflow::Node* ret; |
| 1076 | const auto unique_name = scope.GetUniqueNameForOp("IteratorV2"); |
| 1077 | auto builder = ::tensorflow::NodeBuilder(unique_name, "IteratorV2") |
| 1078 | .Attr("shared_name", shared_name) |
| 1079 | .Attr("container", container) |
| 1080 | .Attr("output_types", output_types) |
| 1081 | .Attr("output_shapes", output_shapes) |
| 1082 | ; |
| 1083 | scope.UpdateBuilder(&builder); |
| 1084 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1085 | if (!scope.ok()) return; |
| 1086 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1087 | this->operation = Operation(ret); |
| 1088 | this->handle = Output(ret, 0); |
| 1089 | } |
| 1090 | |
| 1091 | MapDataset::MapDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
| 1092 | input_dataset, ::tensorflow::InputList other_arguments, |
| 1093 | const NameAttrList& f, const DataTypeSlice& |
| 1094 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
| 1095 | output_shapes, const MapDataset::Attrs& attrs) { |
| 1096 | if (!scope.ok()) return; |
| 1097 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1098 | if (!scope.ok()) return; |
| 1099 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 1100 | if (!scope.ok()) return; |
| 1101 | ::tensorflow::Node* ret; |
| 1102 | const auto unique_name = scope.GetUniqueNameForOp("MapDataset"); |
| 1103 | auto builder = ::tensorflow::NodeBuilder(unique_name, "MapDataset") |
| 1104 | .Input(_input_dataset) |
| 1105 | .Input(_other_arguments) |
| 1106 | .Attr("f", f) |
| 1107 | .Attr("output_types", output_types) |
| 1108 | .Attr("output_shapes", output_shapes) |
| 1109 | .Attr("use_inter_op_parallelism", attrs.use_inter_op_parallelism_) |
| 1110 | .Attr("preserve_cardinality", attrs.preserve_cardinality_) |
| 1111 | .Attr("metadata", attrs.metadata_) |
| 1112 | ; |
| 1113 | scope.UpdateBuilder(&builder); |
| 1114 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1115 | if (!scope.ok()) return; |
| 1116 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1117 | this->operation = Operation(ret); |
| 1118 | this->handle = Output(ret, 0); |
| 1119 | } |
| 1120 | |
| 1121 | MapDataset::MapDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
| 1122 | input_dataset, ::tensorflow::InputList other_arguments, |
| 1123 | const NameAttrList& f, const DataTypeSlice& |
| 1124 | output_types, const gtl::ArraySlice<PartialTensorShape>& |
| 1125 | output_shapes) |
| 1126 | : MapDataset(scope, input_dataset, other_arguments, f, output_types, output_shapes, MapDataset::Attrs()) {} |
| 1127 | |
| 1128 | MapDefun::MapDefun(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
| 1129 | arguments, ::tensorflow::InputList captured_inputs, const |
| 1130 | DataTypeSlice& output_types, const |
| 1131 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
| 1132 | NameAttrList& f, const MapDefun::Attrs& attrs) { |
| 1133 | if (!scope.ok()) return; |
| 1134 | auto _arguments = ::tensorflow::ops::AsNodeOutList(scope, arguments); |
| 1135 | if (!scope.ok()) return; |
| 1136 | auto _captured_inputs = ::tensorflow::ops::AsNodeOutList(scope, captured_inputs); |
| 1137 | if (!scope.ok()) return; |
| 1138 | ::tensorflow::Node* ret; |
| 1139 | const auto unique_name = scope.GetUniqueNameForOp("MapDefun"); |
| 1140 | auto builder = ::tensorflow::NodeBuilder(unique_name, "MapDefun") |
| 1141 | .Input(_arguments) |
| 1142 | .Input(_captured_inputs) |
| 1143 | .Attr("output_types", output_types) |
| 1144 | .Attr("output_shapes", output_shapes) |
| 1145 | .Attr("f", f) |
| 1146 | .Attr("max_intra_op_parallelism", attrs.max_intra_op_parallelism_) |
| 1147 | ; |
| 1148 | scope.UpdateBuilder(&builder); |
| 1149 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1150 | if (!scope.ok()) return; |
| 1151 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1152 | this->operation = Operation(ret); |
| 1153 | for (int32 i = 0; i < ret->num_outputs(); ++i) |
| 1154 | this->output.push_back(Output(ret, i)); |
| 1155 | } |
| 1156 | |
| 1157 | MapDefun::MapDefun(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
| 1158 | arguments, ::tensorflow::InputList captured_inputs, const |
| 1159 | DataTypeSlice& output_types, const |
| 1160 | gtl::ArraySlice<PartialTensorShape>& output_shapes, const |
| 1161 | NameAttrList& f) |
| 1162 | : MapDefun(scope, arguments, captured_inputs, output_types, output_shapes, f, MapDefun::Attrs()) {} |
| 1163 | |
| 1164 | ModelDataset::ModelDataset(const ::tensorflow::Scope& scope, |
| 1165 | ::tensorflow::Input input_dataset, const |
| 1166 | DataTypeSlice& output_types, const |
| 1167 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
| 1168 | const ModelDataset::Attrs& attrs) { |
| 1169 | if (!scope.ok()) return; |
| 1170 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1171 | if (!scope.ok()) return; |
| 1172 | ::tensorflow::Node* ret; |
| 1173 | const auto unique_name = scope.GetUniqueNameForOp("ModelDataset"); |
| 1174 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ModelDataset") |
| 1175 | .Input(_input_dataset) |
| 1176 | .Attr("algorithm", attrs.algorithm_) |
| 1177 | .Attr("cpu_budget", attrs.cpu_budget_) |
| 1178 | .Attr("ram_budget", attrs.ram_budget_) |
| 1179 | .Attr("output_types", output_types) |
| 1180 | .Attr("output_shapes", output_shapes) |
| 1181 | ; |
| 1182 | scope.UpdateBuilder(&builder); |
| 1183 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1184 | if (!scope.ok()) return; |
| 1185 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1186 | this->operation = Operation(ret); |
| 1187 | this->handle = Output(ret, 0); |
| 1188 | } |
| 1189 | |
| 1190 | ModelDataset::ModelDataset(const ::tensorflow::Scope& scope, |
| 1191 | ::tensorflow::Input input_dataset, const |
| 1192 | DataTypeSlice& output_types, const |
| 1193 | gtl::ArraySlice<PartialTensorShape>& output_shapes) |
| 1194 | : ModelDataset(scope, input_dataset, output_types, output_shapes, ModelDataset::Attrs()) {} |
| 1195 | |
| 1196 | MultiDeviceIterator::MultiDeviceIterator(const ::tensorflow::Scope& scope, |
| 1197 | const |
| 1198 | gtl::ArraySlice<::tensorflow::tstring>& |
| 1199 | devices, StringPiece shared_name, |
| 1200 | StringPiece container, const |
| 1201 | DataTypeSlice& output_types, const |
| 1202 | gtl::ArraySlice<PartialTensorShape>& |
| 1203 | output_shapes) { |
| 1204 | if (!scope.ok()) return; |
| 1205 | ::tensorflow::Node* ret; |
| 1206 | const auto unique_name = scope.GetUniqueNameForOp("MultiDeviceIterator"); |
| 1207 | auto builder = ::tensorflow::NodeBuilder(unique_name, "MultiDeviceIterator") |
| 1208 | .Attr("devices", devices) |
| 1209 | .Attr("shared_name", shared_name) |
| 1210 | .Attr("container", container) |
| 1211 | .Attr("output_types", output_types) |
| 1212 | .Attr("output_shapes", output_shapes) |
| 1213 | ; |
| 1214 | scope.UpdateBuilder(&builder); |
| 1215 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1216 | if (!scope.ok()) return; |
| 1217 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1218 | this->operation = Operation(ret); |
| 1219 | this->handle = Output(ret, 0); |
| 1220 | } |
| 1221 | |
| 1222 | MultiDeviceIteratorFromStringHandle::MultiDeviceIteratorFromStringHandle(const |
| 1223 | ::tensorflow::Scope& |
| 1224 | scope, |
| 1225 | ::tensorflow::Input |
| 1226 | string_handle, |
| 1227 | const |
| 1228 | MultiDeviceIteratorFromStringHandle::Attrs& |
| 1229 | attrs) { |
| 1230 | if (!scope.ok()) return; |
| 1231 | auto _string_handle = ::tensorflow::ops::AsNodeOut(scope, string_handle); |
| 1232 | if (!scope.ok()) return; |
| 1233 | ::tensorflow::Node* ret; |
| 1234 | const auto unique_name = scope.GetUniqueNameForOp("MultiDeviceIteratorFromStringHandle"); |
| 1235 | auto builder = ::tensorflow::NodeBuilder(unique_name, "MultiDeviceIteratorFromStringHandle") |
| 1236 | .Input(_string_handle) |
| 1237 | .Attr("output_types", attrs.output_types_) |
| 1238 | .Attr("output_shapes", attrs.output_shapes_) |
| 1239 | ; |
| 1240 | scope.UpdateBuilder(&builder); |
| 1241 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1242 | if (!scope.ok()) return; |
| 1243 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1244 | this->operation = Operation(ret); |
| 1245 | this->multi_device_iterator = Output(ret, 0); |
| 1246 | } |
| 1247 | |
| 1248 | MultiDeviceIteratorFromStringHandle::MultiDeviceIteratorFromStringHandle(const |
| 1249 | ::tensorflow::Scope& |
| 1250 | scope, |
| 1251 | ::tensorflow::Input |
| 1252 | string_handle) |
| 1253 | : MultiDeviceIteratorFromStringHandle(scope, string_handle, MultiDeviceIteratorFromStringHandle::Attrs()) {} |
| 1254 | |
| 1255 | MultiDeviceIteratorGetNextFromShard::MultiDeviceIteratorGetNextFromShard(const |
| 1256 | ::tensorflow::Scope& |
| 1257 | scope, |
| 1258 | ::tensorflow::Input |
| 1259 | multi_device_iterator, |
| 1260 | ::tensorflow::Input |
| 1261 | shard_num, |
| 1262 | ::tensorflow::Input |
| 1263 | incarnation_id, |
| 1264 | const |
| 1265 | DataTypeSlice& |
| 1266 | output_types, |
| 1267 | const |
| 1268 | gtl::ArraySlice<PartialTensorShape>& |
| 1269 | output_shapes) { |
| 1270 | if (!scope.ok()) return; |
| 1271 | auto _multi_device_iterator = ::tensorflow::ops::AsNodeOut(scope, multi_device_iterator); |
| 1272 | if (!scope.ok()) return; |
| 1273 | auto _shard_num = ::tensorflow::ops::AsNodeOut(scope, shard_num); |
| 1274 | if (!scope.ok()) return; |
| 1275 | auto _incarnation_id = ::tensorflow::ops::AsNodeOut(scope, incarnation_id); |
| 1276 | if (!scope.ok()) return; |
| 1277 | ::tensorflow::Node* ret; |
| 1278 | const auto unique_name = scope.GetUniqueNameForOp("MultiDeviceIteratorGetNextFromShard"); |
| 1279 | auto builder = ::tensorflow::NodeBuilder(unique_name, "MultiDeviceIteratorGetNextFromShard") |
| 1280 | .Input(_multi_device_iterator) |
| 1281 | .Input(_shard_num) |
| 1282 | .Input(_incarnation_id) |
| 1283 | .Attr("output_types", output_types) |
| 1284 | .Attr("output_shapes", output_shapes) |
| 1285 | ; |
| 1286 | scope.UpdateBuilder(&builder); |
| 1287 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1288 | if (!scope.ok()) return; |
| 1289 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1290 | this->operation = Operation(ret); |
| 1291 | for (int32 i = 0; i < ret->num_outputs(); ++i) |
| 1292 | this->components.push_back(Output(ret, i)); |
| 1293 | } |
| 1294 | |
| 1295 | MultiDeviceIteratorInit::MultiDeviceIteratorInit(const ::tensorflow::Scope& |
| 1296 | scope, ::tensorflow::Input |
| 1297 | dataset, ::tensorflow::Input |
| 1298 | multi_device_iterator, |
| 1299 | ::tensorflow::Input |
| 1300 | max_buffer_size) { |
| 1301 | if (!scope.ok()) return; |
| 1302 | auto _dataset = ::tensorflow::ops::AsNodeOut(scope, dataset); |
| 1303 | if (!scope.ok()) return; |
| 1304 | auto _multi_device_iterator = ::tensorflow::ops::AsNodeOut(scope, multi_device_iterator); |
| 1305 | if (!scope.ok()) return; |
| 1306 | auto _max_buffer_size = ::tensorflow::ops::AsNodeOut(scope, max_buffer_size); |
| 1307 | if (!scope.ok()) return; |
| 1308 | ::tensorflow::Node* ret; |
| 1309 | const auto unique_name = scope.GetUniqueNameForOp("MultiDeviceIteratorInit"); |
| 1310 | auto builder = ::tensorflow::NodeBuilder(unique_name, "MultiDeviceIteratorInit") |
| 1311 | .Input(_dataset) |
| 1312 | .Input(_multi_device_iterator) |
| 1313 | .Input(_max_buffer_size) |
| 1314 | ; |
| 1315 | scope.UpdateBuilder(&builder); |
| 1316 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1317 | if (!scope.ok()) return; |
| 1318 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1319 | this->operation = Operation(ret); |
| 1320 | this->incarnation_id = Output(ret, 0); |
| 1321 | } |
| 1322 | |
| 1323 | MultiDeviceIteratorToStringHandle::MultiDeviceIteratorToStringHandle(const |
| 1324 | ::tensorflow::Scope& |
| 1325 | scope, |
| 1326 | ::tensorflow::Input |
| 1327 | multi_device_iterator) { |
| 1328 | if (!scope.ok()) return; |
| 1329 | auto _multi_device_iterator = ::tensorflow::ops::AsNodeOut(scope, multi_device_iterator); |
| 1330 | if (!scope.ok()) return; |
| 1331 | ::tensorflow::Node* ret; |
| 1332 | const auto unique_name = scope.GetUniqueNameForOp("MultiDeviceIteratorToStringHandle"); |
| 1333 | auto builder = ::tensorflow::NodeBuilder(unique_name, "MultiDeviceIteratorToStringHandle") |
| 1334 | .Input(_multi_device_iterator) |
| 1335 | ; |
| 1336 | scope.UpdateBuilder(&builder); |
| 1337 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1338 | if (!scope.ok()) return; |
| 1339 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1340 | this->operation = Operation(ret); |
| 1341 | this->string_handle = Output(ret, 0); |
| 1342 | } |
| 1343 | |
| 1344 | OptimizeDataset::OptimizeDataset(const ::tensorflow::Scope& scope, |
| 1345 | ::tensorflow::Input input_dataset, |
| 1346 | ::tensorflow::Input optimizations, const |
| 1347 | DataTypeSlice& output_types, const |
| 1348 | gtl::ArraySlice<PartialTensorShape>& |
| 1349 | output_shapes, const OptimizeDataset::Attrs& |
| 1350 | attrs) { |
| 1351 | if (!scope.ok()) return; |
| 1352 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1353 | if (!scope.ok()) return; |
| 1354 | auto _optimizations = ::tensorflow::ops::AsNodeOut(scope, optimizations); |
| 1355 | if (!scope.ok()) return; |
| 1356 | ::tensorflow::Node* ret; |
| 1357 | const auto unique_name = scope.GetUniqueNameForOp("OptimizeDataset"); |
| 1358 | auto builder = ::tensorflow::NodeBuilder(unique_name, "OptimizeDataset") |
| 1359 | .Input(_input_dataset) |
| 1360 | .Input(_optimizations) |
| 1361 | .Attr("output_types", output_types) |
| 1362 | .Attr("output_shapes", output_shapes) |
| 1363 | .Attr("optimization_configs", attrs.optimization_configs_) |
| 1364 | ; |
| 1365 | scope.UpdateBuilder(&builder); |
| 1366 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1367 | if (!scope.ok()) return; |
| 1368 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1369 | this->operation = Operation(ret); |
| 1370 | this->handle = Output(ret, 0); |
| 1371 | } |
| 1372 | |
| 1373 | OptimizeDataset::OptimizeDataset(const ::tensorflow::Scope& scope, |
| 1374 | ::tensorflow::Input input_dataset, |
| 1375 | ::tensorflow::Input optimizations, const |
| 1376 | DataTypeSlice& output_types, const |
| 1377 | gtl::ArraySlice<PartialTensorShape>& |
| 1378 | output_shapes) |
| 1379 | : OptimizeDataset(scope, input_dataset, optimizations, output_types, output_shapes, OptimizeDataset::Attrs()) {} |
| 1380 | |
| 1381 | OptimizeDatasetV2::OptimizeDatasetV2(const ::tensorflow::Scope& scope, |
| 1382 | ::tensorflow::Input input_dataset, |
| 1383 | ::tensorflow::Input optimizations_enabled, |
| 1384 | ::tensorflow::Input |
| 1385 | optimizations_disabled, |
| 1386 | ::tensorflow::Input optimizations_default, |
| 1387 | const DataTypeSlice& output_types, const |
| 1388 | gtl::ArraySlice<PartialTensorShape>& |
| 1389 | output_shapes, const |
| 1390 | OptimizeDatasetV2::Attrs& attrs) { |
| 1391 | if (!scope.ok()) return; |
| 1392 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1393 | if (!scope.ok()) return; |
| 1394 | auto _optimizations_enabled = ::tensorflow::ops::AsNodeOut(scope, optimizations_enabled); |
| 1395 | if (!scope.ok()) return; |
| 1396 | auto _optimizations_disabled = ::tensorflow::ops::AsNodeOut(scope, optimizations_disabled); |
| 1397 | if (!scope.ok()) return; |
| 1398 | auto _optimizations_default = ::tensorflow::ops::AsNodeOut(scope, optimizations_default); |
| 1399 | if (!scope.ok()) return; |
| 1400 | ::tensorflow::Node* ret; |
| 1401 | const auto unique_name = scope.GetUniqueNameForOp("OptimizeDatasetV2"); |
| 1402 | auto builder = ::tensorflow::NodeBuilder(unique_name, "OptimizeDatasetV2") |
| 1403 | .Input(_input_dataset) |
| 1404 | .Input(_optimizations_enabled) |
| 1405 | .Input(_optimizations_disabled) |
| 1406 | .Input(_optimizations_default) |
| 1407 | .Attr("output_types", output_types) |
| 1408 | .Attr("output_shapes", output_shapes) |
| 1409 | .Attr("optimization_configs", attrs.optimization_configs_) |
| 1410 | ; |
| 1411 | scope.UpdateBuilder(&builder); |
| 1412 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1413 | if (!scope.ok()) return; |
| 1414 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1415 | this->operation = Operation(ret); |
| 1416 | this->handle = Output(ret, 0); |
| 1417 | } |
| 1418 | |
| 1419 | OptimizeDatasetV2::OptimizeDatasetV2(const ::tensorflow::Scope& scope, |
| 1420 | ::tensorflow::Input input_dataset, |
| 1421 | ::tensorflow::Input optimizations_enabled, |
| 1422 | ::tensorflow::Input |
| 1423 | optimizations_disabled, |
| 1424 | ::tensorflow::Input optimizations_default, |
| 1425 | const DataTypeSlice& output_types, const |
| 1426 | gtl::ArraySlice<PartialTensorShape>& |
| 1427 | output_shapes) |
| 1428 | : OptimizeDatasetV2(scope, input_dataset, optimizations_enabled, optimizations_disabled, optimizations_default, output_types, output_shapes, OptimizeDatasetV2::Attrs()) {} |
| 1429 | |
| 1430 | OptionsDataset::OptionsDataset(const ::tensorflow::Scope& scope, |
| 1431 | ::tensorflow::Input input_dataset, StringPiece |
| 1432 | serialized_options, const DataTypeSlice& |
| 1433 | output_types, const |
| 1434 | gtl::ArraySlice<PartialTensorShape>& |
| 1435 | output_shapes, const OptionsDataset::Attrs& |
| 1436 | attrs) { |
| 1437 | if (!scope.ok()) return; |
| 1438 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1439 | if (!scope.ok()) return; |
| 1440 | ::tensorflow::Node* ret; |
| 1441 | const auto unique_name = scope.GetUniqueNameForOp("OptionsDataset"); |
| 1442 | auto builder = ::tensorflow::NodeBuilder(unique_name, "OptionsDataset") |
| 1443 | .Input(_input_dataset) |
| 1444 | .Attr("serialized_options", serialized_options) |
| 1445 | .Attr("output_types", output_types) |
| 1446 | .Attr("output_shapes", output_shapes) |
| 1447 | .Attr("metadata", attrs.metadata_) |
| 1448 | ; |
| 1449 | scope.UpdateBuilder(&builder); |
| 1450 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1451 | if (!scope.ok()) return; |
| 1452 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1453 | this->operation = Operation(ret); |
| 1454 | this->handle = Output(ret, 0); |
| 1455 | } |
| 1456 | |
| 1457 | OptionsDataset::OptionsDataset(const ::tensorflow::Scope& scope, |
| 1458 | ::tensorflow::Input input_dataset, StringPiece |
| 1459 | serialized_options, const DataTypeSlice& |
| 1460 | output_types, const |
| 1461 | gtl::ArraySlice<PartialTensorShape>& |
| 1462 | output_shapes) |
| 1463 | : OptionsDataset(scope, input_dataset, serialized_options, output_types, output_shapes, OptionsDataset::Attrs()) {} |
| 1464 | |
| 1465 | PaddedBatchDataset::PaddedBatchDataset(const ::tensorflow::Scope& scope, |
| 1466 | ::tensorflow::Input input_dataset, |
| 1467 | ::tensorflow::Input batch_size, |
| 1468 | ::tensorflow::InputList padded_shapes, |
| 1469 | ::tensorflow::InputList padding_values, |
| 1470 | const |
| 1471 | gtl::ArraySlice<PartialTensorShape>& |
| 1472 | output_shapes, const |
| 1473 | PaddedBatchDataset::Attrs& attrs) { |
| 1474 | if (!scope.ok()) return; |
| 1475 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1476 | if (!scope.ok()) return; |
| 1477 | auto _batch_size = ::tensorflow::ops::AsNodeOut(scope, batch_size); |
| 1478 | if (!scope.ok()) return; |
| 1479 | auto _padded_shapes = ::tensorflow::ops::AsNodeOutList(scope, padded_shapes); |
| 1480 | if (!scope.ok()) return; |
| 1481 | auto _padding_values = ::tensorflow::ops::AsNodeOutList(scope, padding_values); |
| 1482 | if (!scope.ok()) return; |
| 1483 | ::tensorflow::Node* ret; |
| 1484 | const auto unique_name = scope.GetUniqueNameForOp("PaddedBatchDataset"); |
| 1485 | auto builder = ::tensorflow::NodeBuilder(unique_name, "PaddedBatchDataset") |
| 1486 | .Input(_input_dataset) |
| 1487 | .Input(_batch_size) |
| 1488 | .Input(_padded_shapes) |
| 1489 | .Input(_padding_values) |
| 1490 | .Attr("output_shapes", output_shapes) |
| 1491 | .Attr("metadata", attrs.metadata_) |
| 1492 | ; |
| 1493 | scope.UpdateBuilder(&builder); |
| 1494 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1495 | if (!scope.ok()) return; |
| 1496 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1497 | this->operation = Operation(ret); |
| 1498 | this->handle = Output(ret, 0); |
| 1499 | } |
| 1500 | |
| 1501 | PaddedBatchDataset::PaddedBatchDataset(const ::tensorflow::Scope& scope, |
| 1502 | ::tensorflow::Input input_dataset, |
| 1503 | ::tensorflow::Input batch_size, |
| 1504 | ::tensorflow::InputList padded_shapes, |
| 1505 | ::tensorflow::InputList padding_values, |
| 1506 | const |
| 1507 | gtl::ArraySlice<PartialTensorShape>& |
| 1508 | output_shapes) |
| 1509 | : PaddedBatchDataset(scope, input_dataset, batch_size, padded_shapes, padding_values, output_shapes, PaddedBatchDataset::Attrs()) {} |
| 1510 | |
| 1511 | PaddedBatchDatasetV2::PaddedBatchDatasetV2(const ::tensorflow::Scope& scope, |
| 1512 | ::tensorflow::Input input_dataset, |
| 1513 | ::tensorflow::Input batch_size, |
| 1514 | ::tensorflow::InputList |
| 1515 | padded_shapes, |
| 1516 | ::tensorflow::InputList |
| 1517 | padding_values, ::tensorflow::Input |
| 1518 | drop_remainder, const |
| 1519 | gtl::ArraySlice<PartialTensorShape>& |
| 1520 | output_shapes, const |
| 1521 | PaddedBatchDatasetV2::Attrs& attrs) { |
| 1522 | if (!scope.ok()) return; |
| 1523 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1524 | if (!scope.ok()) return; |
| 1525 | auto _batch_size = ::tensorflow::ops::AsNodeOut(scope, batch_size); |
| 1526 | if (!scope.ok()) return; |
| 1527 | auto _padded_shapes = ::tensorflow::ops::AsNodeOutList(scope, padded_shapes); |
| 1528 | if (!scope.ok()) return; |
| 1529 | auto _padding_values = ::tensorflow::ops::AsNodeOutList(scope, padding_values); |
| 1530 | if (!scope.ok()) return; |
| 1531 | auto _drop_remainder = ::tensorflow::ops::AsNodeOut(scope, drop_remainder); |
| 1532 | if (!scope.ok()) return; |
| 1533 | ::tensorflow::Node* ret; |
| 1534 | const auto unique_name = scope.GetUniqueNameForOp("PaddedBatchDatasetV2"); |
| 1535 | auto builder = ::tensorflow::NodeBuilder(unique_name, "PaddedBatchDatasetV2") |
| 1536 | .Input(_input_dataset) |
| 1537 | .Input(_batch_size) |
| 1538 | .Input(_padded_shapes) |
| 1539 | .Input(_padding_values) |
| 1540 | .Input(_drop_remainder) |
| 1541 | .Attr("parallel_copy", attrs.parallel_copy_) |
| 1542 | .Attr("output_shapes", output_shapes) |
| 1543 | .Attr("metadata", attrs.metadata_) |
| 1544 | ; |
| 1545 | scope.UpdateBuilder(&builder); |
| 1546 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1547 | if (!scope.ok()) return; |
| 1548 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1549 | this->operation = Operation(ret); |
| 1550 | this->handle = Output(ret, 0); |
| 1551 | } |
| 1552 | |
| 1553 | PaddedBatchDatasetV2::PaddedBatchDatasetV2(const ::tensorflow::Scope& scope, |
| 1554 | ::tensorflow::Input input_dataset, |
| 1555 | ::tensorflow::Input batch_size, |
| 1556 | ::tensorflow::InputList |
| 1557 | padded_shapes, |
| 1558 | ::tensorflow::InputList |
| 1559 | padding_values, ::tensorflow::Input |
| 1560 | drop_remainder, const |
| 1561 | gtl::ArraySlice<PartialTensorShape>& |
| 1562 | output_shapes) |
| 1563 | : PaddedBatchDatasetV2(scope, input_dataset, batch_size, padded_shapes, padding_values, drop_remainder, output_shapes, PaddedBatchDatasetV2::Attrs()) {} |
| 1564 | |
| 1565 | ParallelBatchDataset::ParallelBatchDataset(const ::tensorflow::Scope& scope, |
| 1566 | ::tensorflow::Input input_dataset, |
| 1567 | ::tensorflow::Input batch_size, |
| 1568 | ::tensorflow::Input |
| 1569 | num_parallel_calls, |
| 1570 | ::tensorflow::Input drop_remainder, |
| 1571 | const DataTypeSlice& output_types, |
| 1572 | const |
| 1573 | gtl::ArraySlice<PartialTensorShape>& |
| 1574 | output_shapes, const |
| 1575 | ParallelBatchDataset::Attrs& attrs) { |
| 1576 | if (!scope.ok()) return; |
| 1577 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1578 | if (!scope.ok()) return; |
| 1579 | auto _batch_size = ::tensorflow::ops::AsNodeOut(scope, batch_size); |
| 1580 | if (!scope.ok()) return; |
| 1581 | auto _num_parallel_calls = ::tensorflow::ops::AsNodeOut(scope, num_parallel_calls); |
| 1582 | if (!scope.ok()) return; |
| 1583 | auto _drop_remainder = ::tensorflow::ops::AsNodeOut(scope, drop_remainder); |
| 1584 | if (!scope.ok()) return; |
| 1585 | ::tensorflow::Node* ret; |
| 1586 | const auto unique_name = scope.GetUniqueNameForOp("ParallelBatchDataset"); |
| 1587 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ParallelBatchDataset") |
| 1588 | .Input(_input_dataset) |
| 1589 | .Input(_batch_size) |
| 1590 | .Input(_num_parallel_calls) |
| 1591 | .Input(_drop_remainder) |
| 1592 | .Attr("parallel_copy", attrs.parallel_copy_) |
| 1593 | .Attr("output_types", output_types) |
| 1594 | .Attr("output_shapes", output_shapes) |
| 1595 | .Attr("deterministic", attrs.deterministic_) |
| 1596 | .Attr("metadata", attrs.metadata_) |
| 1597 | ; |
| 1598 | scope.UpdateBuilder(&builder); |
| 1599 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1600 | if (!scope.ok()) return; |
| 1601 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1602 | this->operation = Operation(ret); |
| 1603 | this->handle = Output(ret, 0); |
| 1604 | } |
| 1605 | |
| 1606 | ParallelBatchDataset::ParallelBatchDataset(const ::tensorflow::Scope& scope, |
| 1607 | ::tensorflow::Input input_dataset, |
| 1608 | ::tensorflow::Input batch_size, |
| 1609 | ::tensorflow::Input |
| 1610 | num_parallel_calls, |
| 1611 | ::tensorflow::Input drop_remainder, |
| 1612 | const DataTypeSlice& output_types, |
| 1613 | const |
| 1614 | gtl::ArraySlice<PartialTensorShape>& |
| 1615 | output_shapes) |
| 1616 | : ParallelBatchDataset(scope, input_dataset, batch_size, num_parallel_calls, drop_remainder, output_types, output_shapes, ParallelBatchDataset::Attrs()) {} |
| 1617 | |
| 1618 | ParallelFilterDataset::ParallelFilterDataset(const ::tensorflow::Scope& scope, |
| 1619 | ::tensorflow::Input input_dataset, |
| 1620 | ::tensorflow::InputList |
| 1621 | other_arguments, |
| 1622 | ::tensorflow::Input |
| 1623 | num_parallel_calls, const |
| 1624 | NameAttrList& predicate, const |
| 1625 | DataTypeSlice& output_types, const |
| 1626 | gtl::ArraySlice<PartialTensorShape>& |
| 1627 | output_shapes, const |
| 1628 | ParallelFilterDataset::Attrs& |
| 1629 | attrs) { |
| 1630 | if (!scope.ok()) return; |
| 1631 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1632 | if (!scope.ok()) return; |
| 1633 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 1634 | if (!scope.ok()) return; |
| 1635 | auto _num_parallel_calls = ::tensorflow::ops::AsNodeOut(scope, num_parallel_calls); |
| 1636 | if (!scope.ok()) return; |
| 1637 | ::tensorflow::Node* ret; |
| 1638 | const auto unique_name = scope.GetUniqueNameForOp("ParallelFilterDataset"); |
| 1639 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ParallelFilterDataset") |
| 1640 | .Input(_input_dataset) |
| 1641 | .Input(_other_arguments) |
| 1642 | .Input(_num_parallel_calls) |
| 1643 | .Attr("predicate", predicate) |
| 1644 | .Attr("deterministic", attrs.deterministic_) |
| 1645 | .Attr("output_types", output_types) |
| 1646 | .Attr("output_shapes", output_shapes) |
| 1647 | .Attr("metadata", attrs.metadata_) |
| 1648 | ; |
| 1649 | scope.UpdateBuilder(&builder); |
| 1650 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1651 | if (!scope.ok()) return; |
| 1652 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1653 | this->operation = Operation(ret); |
| 1654 | this->handle = Output(ret, 0); |
| 1655 | } |
| 1656 | |
| 1657 | ParallelFilterDataset::ParallelFilterDataset(const ::tensorflow::Scope& scope, |
| 1658 | ::tensorflow::Input input_dataset, |
| 1659 | ::tensorflow::InputList |
| 1660 | other_arguments, |
| 1661 | ::tensorflow::Input |
| 1662 | num_parallel_calls, const |
| 1663 | NameAttrList& predicate, const |
| 1664 | DataTypeSlice& output_types, const |
| 1665 | gtl::ArraySlice<PartialTensorShape>& |
| 1666 | output_shapes) |
| 1667 | : ParallelFilterDataset(scope, input_dataset, other_arguments, num_parallel_calls, predicate, output_types, output_shapes, ParallelFilterDataset::Attrs()) {} |
| 1668 | |
| 1669 | ParallelInterleaveDatasetV2::ParallelInterleaveDatasetV2(const |
| 1670 | ::tensorflow::Scope& |
| 1671 | scope, |
| 1672 | ::tensorflow::Input |
| 1673 | input_dataset, |
| 1674 | ::tensorflow::InputList |
| 1675 | other_arguments, |
| 1676 | ::tensorflow::Input |
| 1677 | cycle_length, |
| 1678 | ::tensorflow::Input |
| 1679 | block_length, |
| 1680 | ::tensorflow::Input |
| 1681 | num_parallel_calls, |
| 1682 | const NameAttrList& f, |
| 1683 | const DataTypeSlice& |
| 1684 | output_types, const |
| 1685 | gtl::ArraySlice<PartialTensorShape>& |
| 1686 | output_shapes, const |
| 1687 | ParallelInterleaveDatasetV2::Attrs& |
| 1688 | attrs) { |
| 1689 | if (!scope.ok()) return; |
| 1690 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1691 | if (!scope.ok()) return; |
| 1692 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 1693 | if (!scope.ok()) return; |
| 1694 | auto _cycle_length = ::tensorflow::ops::AsNodeOut(scope, cycle_length); |
| 1695 | if (!scope.ok()) return; |
| 1696 | auto _block_length = ::tensorflow::ops::AsNodeOut(scope, block_length); |
| 1697 | if (!scope.ok()) return; |
| 1698 | auto _num_parallel_calls = ::tensorflow::ops::AsNodeOut(scope, num_parallel_calls); |
| 1699 | if (!scope.ok()) return; |
| 1700 | ::tensorflow::Node* ret; |
| 1701 | const auto unique_name = scope.GetUniqueNameForOp("ParallelInterleaveDatasetV2"); |
| 1702 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ParallelInterleaveDatasetV2") |
| 1703 | .Input(_input_dataset) |
| 1704 | .Input(_other_arguments) |
| 1705 | .Input(_cycle_length) |
| 1706 | .Input(_block_length) |
| 1707 | .Input(_num_parallel_calls) |
| 1708 | .Attr("f", f) |
| 1709 | .Attr("output_types", output_types) |
| 1710 | .Attr("output_shapes", output_shapes) |
| 1711 | .Attr("sloppy", attrs.sloppy_) |
| 1712 | .Attr("metadata", attrs.metadata_) |
| 1713 | ; |
| 1714 | scope.UpdateBuilder(&builder); |
| 1715 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1716 | if (!scope.ok()) return; |
| 1717 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1718 | this->operation = Operation(ret); |
| 1719 | this->handle = Output(ret, 0); |
| 1720 | } |
| 1721 | |
| 1722 | ParallelInterleaveDatasetV2::ParallelInterleaveDatasetV2(const |
| 1723 | ::tensorflow::Scope& |
| 1724 | scope, |
| 1725 | ::tensorflow::Input |
| 1726 | input_dataset, |
| 1727 | ::tensorflow::InputList |
| 1728 | other_arguments, |
| 1729 | ::tensorflow::Input |
| 1730 | cycle_length, |
| 1731 | ::tensorflow::Input |
| 1732 | block_length, |
| 1733 | ::tensorflow::Input |
| 1734 | num_parallel_calls, |
| 1735 | const NameAttrList& f, |
| 1736 | const DataTypeSlice& |
| 1737 | output_types, const |
| 1738 | gtl::ArraySlice<PartialTensorShape>& |
| 1739 | output_shapes) |
| 1740 | : ParallelInterleaveDatasetV2(scope, input_dataset, other_arguments, cycle_length, block_length, num_parallel_calls, f, output_types, output_shapes, ParallelInterleaveDatasetV2::Attrs()) {} |
| 1741 | |
| 1742 | ParallelInterleaveDatasetV3::ParallelInterleaveDatasetV3(const |
| 1743 | ::tensorflow::Scope& |
| 1744 | scope, |
| 1745 | ::tensorflow::Input |
| 1746 | input_dataset, |
| 1747 | ::tensorflow::InputList |
| 1748 | other_arguments, |
| 1749 | ::tensorflow::Input |
| 1750 | cycle_length, |
| 1751 | ::tensorflow::Input |
| 1752 | block_length, |
| 1753 | ::tensorflow::Input |
| 1754 | num_parallel_calls, |
| 1755 | const NameAttrList& f, |
| 1756 | const DataTypeSlice& |
| 1757 | output_types, const |
| 1758 | gtl::ArraySlice<PartialTensorShape>& |
| 1759 | output_shapes, const |
| 1760 | ParallelInterleaveDatasetV3::Attrs& |
| 1761 | attrs) { |
| 1762 | if (!scope.ok()) return; |
| 1763 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1764 | if (!scope.ok()) return; |
| 1765 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 1766 | if (!scope.ok()) return; |
| 1767 | auto _cycle_length = ::tensorflow::ops::AsNodeOut(scope, cycle_length); |
| 1768 | if (!scope.ok()) return; |
| 1769 | auto _block_length = ::tensorflow::ops::AsNodeOut(scope, block_length); |
| 1770 | if (!scope.ok()) return; |
| 1771 | auto _num_parallel_calls = ::tensorflow::ops::AsNodeOut(scope, num_parallel_calls); |
| 1772 | if (!scope.ok()) return; |
| 1773 | ::tensorflow::Node* ret; |
| 1774 | const auto unique_name = scope.GetUniqueNameForOp("ParallelInterleaveDatasetV3"); |
| 1775 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ParallelInterleaveDatasetV3") |
| 1776 | .Input(_input_dataset) |
| 1777 | .Input(_other_arguments) |
| 1778 | .Input(_cycle_length) |
| 1779 | .Input(_block_length) |
| 1780 | .Input(_num_parallel_calls) |
| 1781 | .Attr("f", f) |
| 1782 | .Attr("deterministic", attrs.deterministic_) |
| 1783 | .Attr("output_types", output_types) |
| 1784 | .Attr("output_shapes", output_shapes) |
| 1785 | .Attr("metadata", attrs.metadata_) |
| 1786 | ; |
| 1787 | scope.UpdateBuilder(&builder); |
| 1788 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1789 | if (!scope.ok()) return; |
| 1790 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1791 | this->operation = Operation(ret); |
| 1792 | this->handle = Output(ret, 0); |
| 1793 | } |
| 1794 | |
| 1795 | ParallelInterleaveDatasetV3::ParallelInterleaveDatasetV3(const |
| 1796 | ::tensorflow::Scope& |
| 1797 | scope, |
| 1798 | ::tensorflow::Input |
| 1799 | input_dataset, |
| 1800 | ::tensorflow::InputList |
| 1801 | other_arguments, |
| 1802 | ::tensorflow::Input |
| 1803 | cycle_length, |
| 1804 | ::tensorflow::Input |
| 1805 | block_length, |
| 1806 | ::tensorflow::Input |
| 1807 | num_parallel_calls, |
| 1808 | const NameAttrList& f, |
| 1809 | const DataTypeSlice& |
| 1810 | output_types, const |
| 1811 | gtl::ArraySlice<PartialTensorShape>& |
| 1812 | output_shapes) |
| 1813 | : ParallelInterleaveDatasetV3(scope, input_dataset, other_arguments, cycle_length, block_length, num_parallel_calls, f, output_types, output_shapes, ParallelInterleaveDatasetV3::Attrs()) {} |
| 1814 | |
| 1815 | ParallelInterleaveDatasetV4::ParallelInterleaveDatasetV4(const |
| 1816 | ::tensorflow::Scope& |
| 1817 | scope, |
| 1818 | ::tensorflow::Input |
| 1819 | input_dataset, |
| 1820 | ::tensorflow::InputList |
| 1821 | other_arguments, |
| 1822 | ::tensorflow::Input |
| 1823 | cycle_length, |
| 1824 | ::tensorflow::Input |
| 1825 | block_length, |
| 1826 | ::tensorflow::Input |
| 1827 | buffer_output_elements, |
| 1828 | ::tensorflow::Input |
| 1829 | prefetch_input_elements, |
| 1830 | ::tensorflow::Input |
| 1831 | num_parallel_calls, |
| 1832 | const NameAttrList& f, |
| 1833 | const DataTypeSlice& |
| 1834 | output_types, const |
| 1835 | gtl::ArraySlice<PartialTensorShape>& |
| 1836 | output_shapes, const |
| 1837 | ParallelInterleaveDatasetV4::Attrs& |
| 1838 | attrs) { |
| 1839 | if (!scope.ok()) return; |
| 1840 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1841 | if (!scope.ok()) return; |
| 1842 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 1843 | if (!scope.ok()) return; |
| 1844 | auto _cycle_length = ::tensorflow::ops::AsNodeOut(scope, cycle_length); |
| 1845 | if (!scope.ok()) return; |
| 1846 | auto _block_length = ::tensorflow::ops::AsNodeOut(scope, block_length); |
| 1847 | if (!scope.ok()) return; |
| 1848 | auto _buffer_output_elements = ::tensorflow::ops::AsNodeOut(scope, buffer_output_elements); |
| 1849 | if (!scope.ok()) return; |
| 1850 | auto _prefetch_input_elements = ::tensorflow::ops::AsNodeOut(scope, prefetch_input_elements); |
| 1851 | if (!scope.ok()) return; |
| 1852 | auto _num_parallel_calls = ::tensorflow::ops::AsNodeOut(scope, num_parallel_calls); |
| 1853 | if (!scope.ok()) return; |
| 1854 | ::tensorflow::Node* ret; |
| 1855 | const auto unique_name = scope.GetUniqueNameForOp("ParallelInterleaveDatasetV4"); |
| 1856 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ParallelInterleaveDatasetV4") |
| 1857 | .Input(_input_dataset) |
| 1858 | .Input(_other_arguments) |
| 1859 | .Input(_cycle_length) |
| 1860 | .Input(_block_length) |
| 1861 | .Input(_buffer_output_elements) |
| 1862 | .Input(_prefetch_input_elements) |
| 1863 | .Input(_num_parallel_calls) |
| 1864 | .Attr("f", f) |
| 1865 | .Attr("deterministic", attrs.deterministic_) |
| 1866 | .Attr("output_types", output_types) |
| 1867 | .Attr("output_shapes", output_shapes) |
| 1868 | .Attr("metadata", attrs.metadata_) |
| 1869 | ; |
| 1870 | scope.UpdateBuilder(&builder); |
| 1871 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1872 | if (!scope.ok()) return; |
| 1873 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1874 | this->operation = Operation(ret); |
| 1875 | this->handle = Output(ret, 0); |
| 1876 | } |
| 1877 | |
| 1878 | ParallelInterleaveDatasetV4::ParallelInterleaveDatasetV4(const |
| 1879 | ::tensorflow::Scope& |
| 1880 | scope, |
| 1881 | ::tensorflow::Input |
| 1882 | input_dataset, |
| 1883 | ::tensorflow::InputList |
| 1884 | other_arguments, |
| 1885 | ::tensorflow::Input |
| 1886 | cycle_length, |
| 1887 | ::tensorflow::Input |
| 1888 | block_length, |
| 1889 | ::tensorflow::Input |
| 1890 | buffer_output_elements, |
| 1891 | ::tensorflow::Input |
| 1892 | prefetch_input_elements, |
| 1893 | ::tensorflow::Input |
| 1894 | num_parallel_calls, |
| 1895 | const NameAttrList& f, |
| 1896 | const DataTypeSlice& |
| 1897 | output_types, const |
| 1898 | gtl::ArraySlice<PartialTensorShape>& |
| 1899 | output_shapes) |
| 1900 | : ParallelInterleaveDatasetV4(scope, input_dataset, other_arguments, cycle_length, block_length, buffer_output_elements, prefetch_input_elements, num_parallel_calls, f, output_types, output_shapes, ParallelInterleaveDatasetV4::Attrs()) {} |
| 1901 | |
| 1902 | ParallelMapDataset::ParallelMapDataset(const ::tensorflow::Scope& scope, |
| 1903 | ::tensorflow::Input input_dataset, |
| 1904 | ::tensorflow::InputList other_arguments, |
| 1905 | ::tensorflow::Input num_parallel_calls, |
| 1906 | const NameAttrList& f, const |
| 1907 | DataTypeSlice& output_types, const |
| 1908 | gtl::ArraySlice<PartialTensorShape>& |
| 1909 | output_shapes, const |
| 1910 | ParallelMapDataset::Attrs& attrs) { |
| 1911 | if (!scope.ok()) return; |
| 1912 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1913 | if (!scope.ok()) return; |
| 1914 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 1915 | if (!scope.ok()) return; |
| 1916 | auto _num_parallel_calls = ::tensorflow::ops::AsNodeOut(scope, num_parallel_calls); |
| 1917 | if (!scope.ok()) return; |
| 1918 | ::tensorflow::Node* ret; |
| 1919 | const auto unique_name = scope.GetUniqueNameForOp("ParallelMapDataset"); |
| 1920 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ParallelMapDataset") |
| 1921 | .Input(_input_dataset) |
| 1922 | .Input(_other_arguments) |
| 1923 | .Input(_num_parallel_calls) |
| 1924 | .Attr("f", f) |
| 1925 | .Attr("output_types", output_types) |
| 1926 | .Attr("output_shapes", output_shapes) |
| 1927 | .Attr("use_inter_op_parallelism", attrs.use_inter_op_parallelism_) |
| 1928 | .Attr("sloppy", attrs.sloppy_) |
| 1929 | .Attr("preserve_cardinality", attrs.preserve_cardinality_) |
| 1930 | .Attr("metadata", attrs.metadata_) |
| 1931 | ; |
| 1932 | scope.UpdateBuilder(&builder); |
| 1933 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1934 | if (!scope.ok()) return; |
| 1935 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1936 | this->operation = Operation(ret); |
| 1937 | this->handle = Output(ret, 0); |
| 1938 | } |
| 1939 | |
| 1940 | ParallelMapDataset::ParallelMapDataset(const ::tensorflow::Scope& scope, |
| 1941 | ::tensorflow::Input input_dataset, |
| 1942 | ::tensorflow::InputList other_arguments, |
| 1943 | ::tensorflow::Input num_parallel_calls, |
| 1944 | const NameAttrList& f, const |
| 1945 | DataTypeSlice& output_types, const |
| 1946 | gtl::ArraySlice<PartialTensorShape>& |
| 1947 | output_shapes) |
| 1948 | : ParallelMapDataset(scope, input_dataset, other_arguments, num_parallel_calls, f, output_types, output_shapes, ParallelMapDataset::Attrs()) {} |
| 1949 | |
| 1950 | ParallelMapDatasetV2::ParallelMapDatasetV2(const ::tensorflow::Scope& scope, |
| 1951 | ::tensorflow::Input input_dataset, |
| 1952 | ::tensorflow::InputList |
| 1953 | other_arguments, ::tensorflow::Input |
| 1954 | num_parallel_calls, const |
| 1955 | NameAttrList& f, const |
| 1956 | DataTypeSlice& output_types, const |
| 1957 | gtl::ArraySlice<PartialTensorShape>& |
| 1958 | output_shapes, const |
| 1959 | ParallelMapDatasetV2::Attrs& attrs) { |
| 1960 | if (!scope.ok()) return; |
| 1961 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 1962 | if (!scope.ok()) return; |
| 1963 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 1964 | if (!scope.ok()) return; |
| 1965 | auto _num_parallel_calls = ::tensorflow::ops::AsNodeOut(scope, num_parallel_calls); |
| 1966 | if (!scope.ok()) return; |
| 1967 | ::tensorflow::Node* ret; |
| 1968 | const auto unique_name = scope.GetUniqueNameForOp("ParallelMapDatasetV2"); |
| 1969 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ParallelMapDatasetV2") |
| 1970 | .Input(_input_dataset) |
| 1971 | .Input(_other_arguments) |
| 1972 | .Input(_num_parallel_calls) |
| 1973 | .Attr("f", f) |
| 1974 | .Attr("output_types", output_types) |
| 1975 | .Attr("output_shapes", output_shapes) |
| 1976 | .Attr("use_inter_op_parallelism", attrs.use_inter_op_parallelism_) |
| 1977 | .Attr("deterministic", attrs.deterministic_) |
| 1978 | .Attr("preserve_cardinality", attrs.preserve_cardinality_) |
| 1979 | .Attr("metadata", attrs.metadata_) |
| 1980 | ; |
| 1981 | scope.UpdateBuilder(&builder); |
| 1982 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 1983 | if (!scope.ok()) return; |
| 1984 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 1985 | this->operation = Operation(ret); |
| 1986 | this->handle = Output(ret, 0); |
| 1987 | } |
| 1988 | |
| 1989 | ParallelMapDatasetV2::ParallelMapDatasetV2(const ::tensorflow::Scope& scope, |
| 1990 | ::tensorflow::Input input_dataset, |
| 1991 | ::tensorflow::InputList |
| 1992 | other_arguments, ::tensorflow::Input |
| 1993 | num_parallel_calls, const |
| 1994 | NameAttrList& f, const |
| 1995 | DataTypeSlice& output_types, const |
| 1996 | gtl::ArraySlice<PartialTensorShape>& |
| 1997 | output_shapes) |
| 1998 | : ParallelMapDatasetV2(scope, input_dataset, other_arguments, num_parallel_calls, f, output_types, output_shapes, ParallelMapDatasetV2::Attrs()) {} |
| 1999 | |
| 2000 | PrefetchDataset::PrefetchDataset(const ::tensorflow::Scope& scope, |
| 2001 | ::tensorflow::Input input_dataset, |
| 2002 | ::tensorflow::Input buffer_size, const |
| 2003 | DataTypeSlice& output_types, const |
| 2004 | gtl::ArraySlice<PartialTensorShape>& |
| 2005 | output_shapes, const PrefetchDataset::Attrs& |
| 2006 | attrs) { |
| 2007 | if (!scope.ok()) return; |
| 2008 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2009 | if (!scope.ok()) return; |
| 2010 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 2011 | if (!scope.ok()) return; |
| 2012 | ::tensorflow::Node* ret; |
| 2013 | const auto unique_name = scope.GetUniqueNameForOp("PrefetchDataset"); |
| 2014 | auto builder = ::tensorflow::NodeBuilder(unique_name, "PrefetchDataset") |
| 2015 | .Input(_input_dataset) |
| 2016 | .Input(_buffer_size) |
| 2017 | .Attr("output_types", output_types) |
| 2018 | .Attr("output_shapes", output_shapes) |
| 2019 | .Attr("slack_period", attrs.slack_period_) |
| 2020 | .Attr("legacy_autotune", attrs.legacy_autotune_) |
| 2021 | .Attr("buffer_size_min", attrs.buffer_size_min_) |
| 2022 | .Attr("metadata", attrs.metadata_) |
| 2023 | ; |
| 2024 | scope.UpdateBuilder(&builder); |
| 2025 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2026 | if (!scope.ok()) return; |
| 2027 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2028 | this->operation = Operation(ret); |
| 2029 | this->handle = Output(ret, 0); |
| 2030 | } |
| 2031 | |
| 2032 | PrefetchDataset::PrefetchDataset(const ::tensorflow::Scope& scope, |
| 2033 | ::tensorflow::Input input_dataset, |
| 2034 | ::tensorflow::Input buffer_size, const |
| 2035 | DataTypeSlice& output_types, const |
| 2036 | gtl::ArraySlice<PartialTensorShape>& |
| 2037 | output_shapes) |
| 2038 | : PrefetchDataset(scope, input_dataset, buffer_size, output_types, output_shapes, PrefetchDataset::Attrs()) {} |
| 2039 | |
| 2040 | RangeDataset::RangeDataset(const ::tensorflow::Scope& scope, |
| 2041 | ::tensorflow::Input start, ::tensorflow::Input stop, |
| 2042 | ::tensorflow::Input step, const DataTypeSlice& |
| 2043 | output_types, const |
| 2044 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
| 2045 | const RangeDataset::Attrs& attrs) { |
| 2046 | if (!scope.ok()) return; |
| 2047 | auto _start = ::tensorflow::ops::AsNodeOut(scope, start); |
| 2048 | if (!scope.ok()) return; |
| 2049 | auto _stop = ::tensorflow::ops::AsNodeOut(scope, stop); |
| 2050 | if (!scope.ok()) return; |
| 2051 | auto _step = ::tensorflow::ops::AsNodeOut(scope, step); |
| 2052 | if (!scope.ok()) return; |
| 2053 | ::tensorflow::Node* ret; |
| 2054 | const auto unique_name = scope.GetUniqueNameForOp("RangeDataset"); |
| 2055 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RangeDataset") |
| 2056 | .Input(_start) |
| 2057 | .Input(_stop) |
| 2058 | .Input(_step) |
| 2059 | .Attr("output_types", output_types) |
| 2060 | .Attr("output_shapes", output_shapes) |
| 2061 | .Attr("metadata", attrs.metadata_) |
| 2062 | .Attr("replicate_on_split", attrs.replicate_on_split_) |
| 2063 | ; |
| 2064 | scope.UpdateBuilder(&builder); |
| 2065 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2066 | if (!scope.ok()) return; |
| 2067 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2068 | this->operation = Operation(ret); |
| 2069 | this->handle = Output(ret, 0); |
| 2070 | } |
| 2071 | |
| 2072 | RangeDataset::RangeDataset(const ::tensorflow::Scope& scope, |
| 2073 | ::tensorflow::Input start, ::tensorflow::Input stop, |
| 2074 | ::tensorflow::Input step, const DataTypeSlice& |
| 2075 | output_types, const |
| 2076 | gtl::ArraySlice<PartialTensorShape>& output_shapes) |
| 2077 | : RangeDataset(scope, start, stop, step, output_types, output_shapes, RangeDataset::Attrs()) {} |
| 2078 | |
| 2079 | ReduceDataset::ReduceDataset(const ::tensorflow::Scope& scope, |
| 2080 | ::tensorflow::Input input_dataset, |
| 2081 | ::tensorflow::InputList initial_state, |
| 2082 | ::tensorflow::InputList other_arguments, const |
| 2083 | NameAttrList& f, const DataTypeSlice& |
| 2084 | output_types, const |
| 2085 | gtl::ArraySlice<PartialTensorShape>& |
| 2086 | output_shapes, const ReduceDataset::Attrs& attrs) { |
| 2087 | if (!scope.ok()) return; |
| 2088 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2089 | if (!scope.ok()) return; |
| 2090 | auto _initial_state = ::tensorflow::ops::AsNodeOutList(scope, initial_state); |
| 2091 | if (!scope.ok()) return; |
| 2092 | auto _other_arguments = ::tensorflow::ops::AsNodeOutList(scope, other_arguments); |
| 2093 | if (!scope.ok()) return; |
| 2094 | ::tensorflow::Node* ret; |
| 2095 | const auto unique_name = scope.GetUniqueNameForOp("ReduceDataset"); |
| 2096 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ReduceDataset") |
| 2097 | .Input(_input_dataset) |
| 2098 | .Input(_initial_state) |
| 2099 | .Input(_other_arguments) |
| 2100 | .Attr("f", f) |
| 2101 | .Attr("output_types", output_types) |
| 2102 | .Attr("output_shapes", output_shapes) |
| 2103 | .Attr("use_inter_op_parallelism", attrs.use_inter_op_parallelism_) |
| 2104 | .Attr("metadata", attrs.metadata_) |
| 2105 | ; |
| 2106 | scope.UpdateBuilder(&builder); |
| 2107 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2108 | if (!scope.ok()) return; |
| 2109 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2110 | this->operation = Operation(ret); |
| 2111 | for (int32 i = 0; i < ret->num_outputs(); ++i) |
| 2112 | this->components.push_back(Output(ret, i)); |
| 2113 | } |
| 2114 | |
| 2115 | ReduceDataset::ReduceDataset(const ::tensorflow::Scope& scope, |
| 2116 | ::tensorflow::Input input_dataset, |
| 2117 | ::tensorflow::InputList initial_state, |
| 2118 | ::tensorflow::InputList other_arguments, const |
| 2119 | NameAttrList& f, const DataTypeSlice& |
| 2120 | output_types, const |
| 2121 | gtl::ArraySlice<PartialTensorShape>& |
| 2122 | output_shapes) |
| 2123 | : ReduceDataset(scope, input_dataset, initial_state, other_arguments, f, output_types, output_shapes, ReduceDataset::Attrs()) {} |
| 2124 | |
| 2125 | RepeatDataset::RepeatDataset(const ::tensorflow::Scope& scope, |
| 2126 | ::tensorflow::Input input_dataset, |
| 2127 | ::tensorflow::Input count, const DataTypeSlice& |
| 2128 | output_types, const |
| 2129 | gtl::ArraySlice<PartialTensorShape>& |
| 2130 | output_shapes, const RepeatDataset::Attrs& attrs) { |
| 2131 | if (!scope.ok()) return; |
| 2132 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2133 | if (!scope.ok()) return; |
| 2134 | auto _count = ::tensorflow::ops::AsNodeOut(scope, count); |
| 2135 | if (!scope.ok()) return; |
| 2136 | ::tensorflow::Node* ret; |
| 2137 | const auto unique_name = scope.GetUniqueNameForOp("RepeatDataset"); |
| 2138 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RepeatDataset") |
| 2139 | .Input(_input_dataset) |
| 2140 | .Input(_count) |
| 2141 | .Attr("output_types", output_types) |
| 2142 | .Attr("output_shapes", output_shapes) |
| 2143 | .Attr("metadata", attrs.metadata_) |
| 2144 | ; |
| 2145 | scope.UpdateBuilder(&builder); |
| 2146 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2147 | if (!scope.ok()) return; |
| 2148 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2149 | this->operation = Operation(ret); |
| 2150 | this->handle = Output(ret, 0); |
| 2151 | } |
| 2152 | |
| 2153 | RepeatDataset::RepeatDataset(const ::tensorflow::Scope& scope, |
| 2154 | ::tensorflow::Input input_dataset, |
| 2155 | ::tensorflow::Input count, const DataTypeSlice& |
| 2156 | output_types, const |
| 2157 | gtl::ArraySlice<PartialTensorShape>& |
| 2158 | output_shapes) |
| 2159 | : RepeatDataset(scope, input_dataset, count, output_types, output_shapes, RepeatDataset::Attrs()) {} |
| 2160 | |
| 2161 | RewriteDataset::RewriteDataset(const ::tensorflow::Scope& scope, |
| 2162 | ::tensorflow::Input input_dataset, |
| 2163 | ::tensorflow::Input rewrite_name, const |
| 2164 | DataTypeSlice& output_types, const |
| 2165 | gtl::ArraySlice<PartialTensorShape>& |
| 2166 | output_shapes) { |
| 2167 | if (!scope.ok()) return; |
| 2168 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2169 | if (!scope.ok()) return; |
| 2170 | auto _rewrite_name = ::tensorflow::ops::AsNodeOut(scope, rewrite_name); |
| 2171 | if (!scope.ok()) return; |
| 2172 | ::tensorflow::Node* ret; |
| 2173 | const auto unique_name = scope.GetUniqueNameForOp("RewriteDataset"); |
| 2174 | auto builder = ::tensorflow::NodeBuilder(unique_name, "RewriteDataset") |
| 2175 | .Input(_input_dataset) |
| 2176 | .Input(_rewrite_name) |
| 2177 | .Attr("output_types", output_types) |
| 2178 | .Attr("output_shapes", output_shapes) |
| 2179 | ; |
| 2180 | scope.UpdateBuilder(&builder); |
| 2181 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2182 | if (!scope.ok()) return; |
| 2183 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2184 | this->operation = Operation(ret); |
| 2185 | this->handle = Output(ret, 0); |
| 2186 | } |
| 2187 | |
| 2188 | ShardDataset::ShardDataset(const ::tensorflow::Scope& scope, |
| 2189 | ::tensorflow::Input input_dataset, |
| 2190 | ::tensorflow::Input num_shards, ::tensorflow::Input |
| 2191 | index, const DataTypeSlice& output_types, const |
| 2192 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
| 2193 | const ShardDataset::Attrs& attrs) { |
| 2194 | if (!scope.ok()) return; |
| 2195 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2196 | if (!scope.ok()) return; |
| 2197 | auto _num_shards = ::tensorflow::ops::AsNodeOut(scope, num_shards); |
| 2198 | if (!scope.ok()) return; |
| 2199 | auto _index = ::tensorflow::ops::AsNodeOut(scope, index); |
| 2200 | if (!scope.ok()) return; |
| 2201 | ::tensorflow::Node* ret; |
| 2202 | const auto unique_name = scope.GetUniqueNameForOp("ShardDataset"); |
| 2203 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ShardDataset") |
| 2204 | .Input(_input_dataset) |
| 2205 | .Input(_num_shards) |
| 2206 | .Input(_index) |
| 2207 | .Attr("require_non_empty", attrs.require_non_empty_) |
| 2208 | .Attr("output_types", output_types) |
| 2209 | .Attr("output_shapes", output_shapes) |
| 2210 | .Attr("metadata", attrs.metadata_) |
| 2211 | ; |
| 2212 | scope.UpdateBuilder(&builder); |
| 2213 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2214 | if (!scope.ok()) return; |
| 2215 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2216 | this->operation = Operation(ret); |
| 2217 | this->handle = Output(ret, 0); |
| 2218 | } |
| 2219 | |
| 2220 | ShardDataset::ShardDataset(const ::tensorflow::Scope& scope, |
| 2221 | ::tensorflow::Input input_dataset, |
| 2222 | ::tensorflow::Input num_shards, ::tensorflow::Input |
| 2223 | index, const DataTypeSlice& output_types, const |
| 2224 | gtl::ArraySlice<PartialTensorShape>& output_shapes) |
| 2225 | : ShardDataset(scope, input_dataset, num_shards, index, output_types, output_shapes, ShardDataset::Attrs()) {} |
| 2226 | |
| 2227 | ShuffleAndRepeatDataset::ShuffleAndRepeatDataset(const ::tensorflow::Scope& |
| 2228 | scope, ::tensorflow::Input |
| 2229 | input_dataset, |
| 2230 | ::tensorflow::Input |
| 2231 | buffer_size, |
| 2232 | ::tensorflow::Input seed, |
| 2233 | ::tensorflow::Input seed2, |
| 2234 | ::tensorflow::Input count, |
| 2235 | const DataTypeSlice& |
| 2236 | output_types, const |
| 2237 | gtl::ArraySlice<PartialTensorShape>& |
| 2238 | output_shapes, const |
| 2239 | ShuffleAndRepeatDataset::Attrs& |
| 2240 | attrs) { |
| 2241 | if (!scope.ok()) return; |
| 2242 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2243 | if (!scope.ok()) return; |
| 2244 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 2245 | if (!scope.ok()) return; |
| 2246 | auto _seed = ::tensorflow::ops::AsNodeOut(scope, seed); |
| 2247 | if (!scope.ok()) return; |
| 2248 | auto _seed2 = ::tensorflow::ops::AsNodeOut(scope, seed2); |
| 2249 | if (!scope.ok()) return; |
| 2250 | auto _count = ::tensorflow::ops::AsNodeOut(scope, count); |
| 2251 | if (!scope.ok()) return; |
| 2252 | ::tensorflow::Node* ret; |
| 2253 | const auto unique_name = scope.GetUniqueNameForOp("ShuffleAndRepeatDataset"); |
| 2254 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ShuffleAndRepeatDataset") |
| 2255 | .Input(_input_dataset) |
| 2256 | .Input(_buffer_size) |
| 2257 | .Input(_seed) |
| 2258 | .Input(_seed2) |
| 2259 | .Input(_count) |
| 2260 | .Attr("output_types", output_types) |
| 2261 | .Attr("output_shapes", output_shapes) |
| 2262 | .Attr("reshuffle_each_iteration", attrs.reshuffle_each_iteration_) |
| 2263 | .Attr("metadata", attrs.metadata_) |
| 2264 | ; |
| 2265 | scope.UpdateBuilder(&builder); |
| 2266 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2267 | if (!scope.ok()) return; |
| 2268 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2269 | this->operation = Operation(ret); |
| 2270 | this->handle = Output(ret, 0); |
| 2271 | } |
| 2272 | |
| 2273 | ShuffleAndRepeatDataset::ShuffleAndRepeatDataset(const ::tensorflow::Scope& |
| 2274 | scope, ::tensorflow::Input |
| 2275 | input_dataset, |
| 2276 | ::tensorflow::Input |
| 2277 | buffer_size, |
| 2278 | ::tensorflow::Input seed, |
| 2279 | ::tensorflow::Input seed2, |
| 2280 | ::tensorflow::Input count, |
| 2281 | const DataTypeSlice& |
| 2282 | output_types, const |
| 2283 | gtl::ArraySlice<PartialTensorShape>& |
| 2284 | output_shapes) |
| 2285 | : ShuffleAndRepeatDataset(scope, input_dataset, buffer_size, seed, seed2, count, output_types, output_shapes, ShuffleAndRepeatDataset::Attrs()) {} |
| 2286 | |
| 2287 | ShuffleAndRepeatDatasetV2::ShuffleAndRepeatDatasetV2(const ::tensorflow::Scope& |
| 2288 | scope, ::tensorflow::Input |
| 2289 | input_dataset, |
| 2290 | ::tensorflow::Input |
| 2291 | buffer_size, |
| 2292 | ::tensorflow::Input seed, |
| 2293 | ::tensorflow::Input seed2, |
| 2294 | ::tensorflow::Input count, |
| 2295 | ::tensorflow::Input |
| 2296 | seed_generator, const |
| 2297 | DataTypeSlice& |
| 2298 | output_types, const |
| 2299 | gtl::ArraySlice<PartialTensorShape>& |
| 2300 | output_shapes, const |
| 2301 | ShuffleAndRepeatDatasetV2::Attrs& |
| 2302 | attrs) { |
| 2303 | if (!scope.ok()) return; |
| 2304 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2305 | if (!scope.ok()) return; |
| 2306 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 2307 | if (!scope.ok()) return; |
| 2308 | auto _seed = ::tensorflow::ops::AsNodeOut(scope, seed); |
| 2309 | if (!scope.ok()) return; |
| 2310 | auto _seed2 = ::tensorflow::ops::AsNodeOut(scope, seed2); |
| 2311 | if (!scope.ok()) return; |
| 2312 | auto _count = ::tensorflow::ops::AsNodeOut(scope, count); |
| 2313 | if (!scope.ok()) return; |
| 2314 | auto _seed_generator = ::tensorflow::ops::AsNodeOut(scope, seed_generator); |
| 2315 | if (!scope.ok()) return; |
| 2316 | ::tensorflow::Node* ret; |
| 2317 | const auto unique_name = scope.GetUniqueNameForOp("ShuffleAndRepeatDatasetV2"); |
| 2318 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ShuffleAndRepeatDatasetV2") |
| 2319 | .Input(_input_dataset) |
| 2320 | .Input(_buffer_size) |
| 2321 | .Input(_seed) |
| 2322 | .Input(_seed2) |
| 2323 | .Input(_count) |
| 2324 | .Input(_seed_generator) |
| 2325 | .Attr("reshuffle_each_iteration", attrs.reshuffle_each_iteration_) |
| 2326 | .Attr("output_types", output_types) |
| 2327 | .Attr("output_shapes", output_shapes) |
| 2328 | .Attr("metadata", attrs.metadata_) |
| 2329 | ; |
| 2330 | scope.UpdateBuilder(&builder); |
| 2331 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2332 | if (!scope.ok()) return; |
| 2333 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2334 | this->operation = Operation(ret); |
| 2335 | this->handle = Output(ret, 0); |
| 2336 | } |
| 2337 | |
| 2338 | ShuffleAndRepeatDatasetV2::ShuffleAndRepeatDatasetV2(const ::tensorflow::Scope& |
| 2339 | scope, ::tensorflow::Input |
| 2340 | input_dataset, |
| 2341 | ::tensorflow::Input |
| 2342 | buffer_size, |
| 2343 | ::tensorflow::Input seed, |
| 2344 | ::tensorflow::Input seed2, |
| 2345 | ::tensorflow::Input count, |
| 2346 | ::tensorflow::Input |
| 2347 | seed_generator, const |
| 2348 | DataTypeSlice& |
| 2349 | output_types, const |
| 2350 | gtl::ArraySlice<PartialTensorShape>& |
| 2351 | output_shapes) |
| 2352 | : ShuffleAndRepeatDatasetV2(scope, input_dataset, buffer_size, seed, seed2, count, seed_generator, output_types, output_shapes, ShuffleAndRepeatDatasetV2::Attrs()) {} |
| 2353 | |
| 2354 | ShuffleDataset::ShuffleDataset(const ::tensorflow::Scope& scope, |
| 2355 | ::tensorflow::Input input_dataset, |
| 2356 | ::tensorflow::Input buffer_size, |
| 2357 | ::tensorflow::Input seed, ::tensorflow::Input |
| 2358 | seed2, const DataTypeSlice& output_types, const |
| 2359 | gtl::ArraySlice<PartialTensorShape>& |
| 2360 | output_shapes, const ShuffleDataset::Attrs& |
| 2361 | attrs) { |
| 2362 | if (!scope.ok()) return; |
| 2363 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2364 | if (!scope.ok()) return; |
| 2365 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 2366 | if (!scope.ok()) return; |
| 2367 | auto _seed = ::tensorflow::ops::AsNodeOut(scope, seed); |
| 2368 | if (!scope.ok()) return; |
| 2369 | auto _seed2 = ::tensorflow::ops::AsNodeOut(scope, seed2); |
| 2370 | if (!scope.ok()) return; |
| 2371 | ::tensorflow::Node* ret; |
| 2372 | const auto unique_name = scope.GetUniqueNameForOp("ShuffleDataset"); |
| 2373 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ShuffleDataset") |
| 2374 | .Input(_input_dataset) |
| 2375 | .Input(_buffer_size) |
| 2376 | .Input(_seed) |
| 2377 | .Input(_seed2) |
| 2378 | .Attr("reshuffle_each_iteration", attrs.reshuffle_each_iteration_) |
| 2379 | .Attr("output_types", output_types) |
| 2380 | .Attr("output_shapes", output_shapes) |
| 2381 | .Attr("metadata", attrs.metadata_) |
| 2382 | ; |
| 2383 | scope.UpdateBuilder(&builder); |
| 2384 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2385 | if (!scope.ok()) return; |
| 2386 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2387 | this->operation = Operation(ret); |
| 2388 | this->handle = Output(ret, 0); |
| 2389 | } |
| 2390 | |
| 2391 | ShuffleDataset::ShuffleDataset(const ::tensorflow::Scope& scope, |
| 2392 | ::tensorflow::Input input_dataset, |
| 2393 | ::tensorflow::Input buffer_size, |
| 2394 | ::tensorflow::Input seed, ::tensorflow::Input |
| 2395 | seed2, const DataTypeSlice& output_types, const |
| 2396 | gtl::ArraySlice<PartialTensorShape>& |
| 2397 | output_shapes) |
| 2398 | : ShuffleDataset(scope, input_dataset, buffer_size, seed, seed2, output_types, output_shapes, ShuffleDataset::Attrs()) {} |
| 2399 | |
| 2400 | ShuffleDatasetV2::ShuffleDatasetV2(const ::tensorflow::Scope& scope, |
| 2401 | ::tensorflow::Input input_dataset, |
| 2402 | ::tensorflow::Input buffer_size, |
| 2403 | ::tensorflow::Input seed_generator, const |
| 2404 | DataTypeSlice& output_types, const |
| 2405 | gtl::ArraySlice<PartialTensorShape>& |
| 2406 | output_shapes, const |
| 2407 | ShuffleDatasetV2::Attrs& attrs) { |
| 2408 | if (!scope.ok()) return; |
| 2409 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2410 | if (!scope.ok()) return; |
| 2411 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 2412 | if (!scope.ok()) return; |
| 2413 | auto _seed_generator = ::tensorflow::ops::AsNodeOut(scope, seed_generator); |
| 2414 | if (!scope.ok()) return; |
| 2415 | ::tensorflow::Node* ret; |
| 2416 | const auto unique_name = scope.GetUniqueNameForOp("ShuffleDatasetV2"); |
| 2417 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ShuffleDatasetV2") |
| 2418 | .Input(_input_dataset) |
| 2419 | .Input(_buffer_size) |
| 2420 | .Input(_seed_generator) |
| 2421 | .Attr("output_types", output_types) |
| 2422 | .Attr("output_shapes", output_shapes) |
| 2423 | .Attr("metadata", attrs.metadata_) |
| 2424 | ; |
| 2425 | scope.UpdateBuilder(&builder); |
| 2426 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2427 | if (!scope.ok()) return; |
| 2428 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2429 | this->operation = Operation(ret); |
| 2430 | this->handle = Output(ret, 0); |
| 2431 | } |
| 2432 | |
| 2433 | ShuffleDatasetV2::ShuffleDatasetV2(const ::tensorflow::Scope& scope, |
| 2434 | ::tensorflow::Input input_dataset, |
| 2435 | ::tensorflow::Input buffer_size, |
| 2436 | ::tensorflow::Input seed_generator, const |
| 2437 | DataTypeSlice& output_types, const |
| 2438 | gtl::ArraySlice<PartialTensorShape>& |
| 2439 | output_shapes) |
| 2440 | : ShuffleDatasetV2(scope, input_dataset, buffer_size, seed_generator, output_types, output_shapes, ShuffleDatasetV2::Attrs()) {} |
| 2441 | |
| 2442 | ShuffleDatasetV3::ShuffleDatasetV3(const ::tensorflow::Scope& scope, |
| 2443 | ::tensorflow::Input input_dataset, |
| 2444 | ::tensorflow::Input buffer_size, |
| 2445 | ::tensorflow::Input seed, |
| 2446 | ::tensorflow::Input seed2, |
| 2447 | ::tensorflow::Input seed_generator, const |
| 2448 | DataTypeSlice& output_types, const |
| 2449 | gtl::ArraySlice<PartialTensorShape>& |
| 2450 | output_shapes, const |
| 2451 | ShuffleDatasetV3::Attrs& attrs) { |
| 2452 | if (!scope.ok()) return; |
| 2453 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2454 | if (!scope.ok()) return; |
| 2455 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 2456 | if (!scope.ok()) return; |
| 2457 | auto _seed = ::tensorflow::ops::AsNodeOut(scope, seed); |
| 2458 | if (!scope.ok()) return; |
| 2459 | auto _seed2 = ::tensorflow::ops::AsNodeOut(scope, seed2); |
| 2460 | if (!scope.ok()) return; |
| 2461 | auto _seed_generator = ::tensorflow::ops::AsNodeOut(scope, seed_generator); |
| 2462 | if (!scope.ok()) return; |
| 2463 | ::tensorflow::Node* ret; |
| 2464 | const auto unique_name = scope.GetUniqueNameForOp("ShuffleDatasetV3"); |
| 2465 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ShuffleDatasetV3") |
| 2466 | .Input(_input_dataset) |
| 2467 | .Input(_buffer_size) |
| 2468 | .Input(_seed) |
| 2469 | .Input(_seed2) |
| 2470 | .Input(_seed_generator) |
| 2471 | .Attr("reshuffle_each_iteration", attrs.reshuffle_each_iteration_) |
| 2472 | .Attr("output_types", output_types) |
| 2473 | .Attr("output_shapes", output_shapes) |
| 2474 | .Attr("metadata", attrs.metadata_) |
| 2475 | ; |
| 2476 | scope.UpdateBuilder(&builder); |
| 2477 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2478 | if (!scope.ok()) return; |
| 2479 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2480 | this->operation = Operation(ret); |
| 2481 | this->handle = Output(ret, 0); |
| 2482 | } |
| 2483 | |
| 2484 | ShuffleDatasetV3::ShuffleDatasetV3(const ::tensorflow::Scope& scope, |
| 2485 | ::tensorflow::Input input_dataset, |
| 2486 | ::tensorflow::Input buffer_size, |
| 2487 | ::tensorflow::Input seed, |
| 2488 | ::tensorflow::Input seed2, |
| 2489 | ::tensorflow::Input seed_generator, const |
| 2490 | DataTypeSlice& output_types, const |
| 2491 | gtl::ArraySlice<PartialTensorShape>& |
| 2492 | output_shapes) |
| 2493 | : ShuffleDatasetV3(scope, input_dataset, buffer_size, seed, seed2, seed_generator, output_types, output_shapes, ShuffleDatasetV3::Attrs()) {} |
| 2494 | |
| 2495 | SkipDataset::SkipDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
| 2496 | input_dataset, ::tensorflow::Input count, const |
| 2497 | DataTypeSlice& output_types, const |
| 2498 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
| 2499 | const SkipDataset::Attrs& attrs) { |
| 2500 | if (!scope.ok()) return; |
| 2501 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2502 | if (!scope.ok()) return; |
| 2503 | auto _count = ::tensorflow::ops::AsNodeOut(scope, count); |
| 2504 | if (!scope.ok()) return; |
| 2505 | ::tensorflow::Node* ret; |
| 2506 | const auto unique_name = scope.GetUniqueNameForOp("SkipDataset"); |
| 2507 | auto builder = ::tensorflow::NodeBuilder(unique_name, "SkipDataset") |
| 2508 | .Input(_input_dataset) |
| 2509 | .Input(_count) |
| 2510 | .Attr("output_types", output_types) |
| 2511 | .Attr("output_shapes", output_shapes) |
| 2512 | .Attr("metadata", attrs.metadata_) |
| 2513 | ; |
| 2514 | scope.UpdateBuilder(&builder); |
| 2515 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2516 | if (!scope.ok()) return; |
| 2517 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2518 | this->operation = Operation(ret); |
| 2519 | this->handle = Output(ret, 0); |
| 2520 | } |
| 2521 | |
| 2522 | SkipDataset::SkipDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
| 2523 | input_dataset, ::tensorflow::Input count, const |
| 2524 | DataTypeSlice& output_types, const |
| 2525 | gtl::ArraySlice<PartialTensorShape>& output_shapes) |
| 2526 | : SkipDataset(scope, input_dataset, count, output_types, output_shapes, SkipDataset::Attrs()) {} |
| 2527 | |
| 2528 | SparseTensorSliceDataset::SparseTensorSliceDataset(const ::tensorflow::Scope& |
| 2529 | scope, ::tensorflow::Input |
| 2530 | indices, ::tensorflow::Input |
| 2531 | values, ::tensorflow::Input |
| 2532 | dense_shape) { |
| 2533 | if (!scope.ok()) return; |
| 2534 | auto _indices = ::tensorflow::ops::AsNodeOut(scope, indices); |
| 2535 | if (!scope.ok()) return; |
| 2536 | auto _values = ::tensorflow::ops::AsNodeOut(scope, values); |
| 2537 | if (!scope.ok()) return; |
| 2538 | auto _dense_shape = ::tensorflow::ops::AsNodeOut(scope, dense_shape); |
| 2539 | if (!scope.ok()) return; |
| 2540 | ::tensorflow::Node* ret; |
| 2541 | const auto unique_name = scope.GetUniqueNameForOp("SparseTensorSliceDataset"); |
| 2542 | auto builder = ::tensorflow::NodeBuilder(unique_name, "SparseTensorSliceDataset") |
| 2543 | .Input(_indices) |
| 2544 | .Input(_values) |
| 2545 | .Input(_dense_shape) |
| 2546 | ; |
| 2547 | scope.UpdateBuilder(&builder); |
| 2548 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2549 | if (!scope.ok()) return; |
| 2550 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2551 | this->operation = Operation(ret); |
| 2552 | this->handle = Output(ret, 0); |
| 2553 | } |
| 2554 | |
| 2555 | TFRecordDataset::TFRecordDataset(const ::tensorflow::Scope& scope, |
| 2556 | ::tensorflow::Input filenames, |
| 2557 | ::tensorflow::Input compression_type, |
| 2558 | ::tensorflow::Input buffer_size, const |
| 2559 | TFRecordDataset::Attrs& attrs) { |
| 2560 | if (!scope.ok()) return; |
| 2561 | auto _filenames = ::tensorflow::ops::AsNodeOut(scope, filenames); |
| 2562 | if (!scope.ok()) return; |
| 2563 | auto _compression_type = ::tensorflow::ops::AsNodeOut(scope, compression_type); |
| 2564 | if (!scope.ok()) return; |
| 2565 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 2566 | if (!scope.ok()) return; |
| 2567 | ::tensorflow::Node* ret; |
| 2568 | const auto unique_name = scope.GetUniqueNameForOp("TFRecordDataset"); |
| 2569 | auto builder = ::tensorflow::NodeBuilder(unique_name, "TFRecordDataset") |
| 2570 | .Input(_filenames) |
| 2571 | .Input(_compression_type) |
| 2572 | .Input(_buffer_size) |
| 2573 | .Attr("metadata", attrs.metadata_) |
| 2574 | ; |
| 2575 | scope.UpdateBuilder(&builder); |
| 2576 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2577 | if (!scope.ok()) return; |
| 2578 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2579 | this->operation = Operation(ret); |
| 2580 | this->handle = Output(ret, 0); |
| 2581 | } |
| 2582 | |
| 2583 | TFRecordDataset::TFRecordDataset(const ::tensorflow::Scope& scope, |
| 2584 | ::tensorflow::Input filenames, |
| 2585 | ::tensorflow::Input compression_type, |
| 2586 | ::tensorflow::Input buffer_size) |
| 2587 | : TFRecordDataset(scope, filenames, compression_type, buffer_size, TFRecordDataset::Attrs()) {} |
| 2588 | |
| 2589 | TakeDataset::TakeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
| 2590 | input_dataset, ::tensorflow::Input count, const |
| 2591 | DataTypeSlice& output_types, const |
| 2592 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
| 2593 | const TakeDataset::Attrs& attrs) { |
| 2594 | if (!scope.ok()) return; |
| 2595 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2596 | if (!scope.ok()) return; |
| 2597 | auto _count = ::tensorflow::ops::AsNodeOut(scope, count); |
| 2598 | if (!scope.ok()) return; |
| 2599 | ::tensorflow::Node* ret; |
| 2600 | const auto unique_name = scope.GetUniqueNameForOp("TakeDataset"); |
| 2601 | auto builder = ::tensorflow::NodeBuilder(unique_name, "TakeDataset") |
| 2602 | .Input(_input_dataset) |
| 2603 | .Input(_count) |
| 2604 | .Attr("output_types", output_types) |
| 2605 | .Attr("output_shapes", output_shapes) |
| 2606 | .Attr("metadata", attrs.metadata_) |
| 2607 | ; |
| 2608 | scope.UpdateBuilder(&builder); |
| 2609 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2610 | if (!scope.ok()) return; |
| 2611 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2612 | this->operation = Operation(ret); |
| 2613 | this->handle = Output(ret, 0); |
| 2614 | } |
| 2615 | |
| 2616 | TakeDataset::TakeDataset(const ::tensorflow::Scope& scope, ::tensorflow::Input |
| 2617 | input_dataset, ::tensorflow::Input count, const |
| 2618 | DataTypeSlice& output_types, const |
| 2619 | gtl::ArraySlice<PartialTensorShape>& output_shapes) |
| 2620 | : TakeDataset(scope, input_dataset, count, output_types, output_shapes, TakeDataset::Attrs()) {} |
| 2621 | |
| 2622 | TensorDataset::TensorDataset(const ::tensorflow::Scope& scope, |
| 2623 | ::tensorflow::InputList components, const |
| 2624 | gtl::ArraySlice<PartialTensorShape>& |
| 2625 | output_shapes, const TensorDataset::Attrs& attrs) { |
| 2626 | if (!scope.ok()) return; |
| 2627 | auto _components = ::tensorflow::ops::AsNodeOutList(scope, components); |
| 2628 | if (!scope.ok()) return; |
| 2629 | ::tensorflow::Node* ret; |
| 2630 | const auto unique_name = scope.GetUniqueNameForOp("TensorDataset"); |
| 2631 | auto builder = ::tensorflow::NodeBuilder(unique_name, "TensorDataset") |
| 2632 | .Input(_components) |
| 2633 | .Attr("output_shapes", output_shapes) |
| 2634 | .Attr("metadata", attrs.metadata_) |
| 2635 | ; |
| 2636 | scope.UpdateBuilder(&builder); |
| 2637 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2638 | if (!scope.ok()) return; |
| 2639 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2640 | this->operation = Operation(ret); |
| 2641 | this->handle = Output(ret, 0); |
| 2642 | } |
| 2643 | |
| 2644 | TensorDataset::TensorDataset(const ::tensorflow::Scope& scope, |
| 2645 | ::tensorflow::InputList components, const |
| 2646 | gtl::ArraySlice<PartialTensorShape>& |
| 2647 | output_shapes) |
| 2648 | : TensorDataset(scope, components, output_shapes, TensorDataset::Attrs()) {} |
| 2649 | |
| 2650 | TensorSliceDataset::TensorSliceDataset(const ::tensorflow::Scope& scope, |
| 2651 | ::tensorflow::InputList components, |
| 2652 | const |
| 2653 | gtl::ArraySlice<PartialTensorShape>& |
| 2654 | output_shapes, const |
| 2655 | TensorSliceDataset::Attrs& attrs) { |
| 2656 | if (!scope.ok()) return; |
| 2657 | auto _components = ::tensorflow::ops::AsNodeOutList(scope, components); |
| 2658 | if (!scope.ok()) return; |
| 2659 | ::tensorflow::Node* ret; |
| 2660 | const auto unique_name = scope.GetUniqueNameForOp("TensorSliceDataset"); |
| 2661 | auto builder = ::tensorflow::NodeBuilder(unique_name, "TensorSliceDataset") |
| 2662 | .Input(_components) |
| 2663 | .Attr("output_shapes", output_shapes) |
| 2664 | .Attr("is_files", attrs.is_files_) |
| 2665 | .Attr("metadata", attrs.metadata_) |
| 2666 | .Attr("replicate_on_split", attrs.replicate_on_split_) |
| 2667 | ; |
| 2668 | scope.UpdateBuilder(&builder); |
| 2669 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2670 | if (!scope.ok()) return; |
| 2671 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2672 | this->operation = Operation(ret); |
| 2673 | this->handle = Output(ret, 0); |
| 2674 | } |
| 2675 | |
| 2676 | TensorSliceDataset::TensorSliceDataset(const ::tensorflow::Scope& scope, |
| 2677 | ::tensorflow::InputList components, |
| 2678 | const |
| 2679 | gtl::ArraySlice<PartialTensorShape>& |
| 2680 | output_shapes) |
| 2681 | : TensorSliceDataset(scope, components, output_shapes, TensorSliceDataset::Attrs()) {} |
| 2682 | |
| 2683 | TextLineDataset::TextLineDataset(const ::tensorflow::Scope& scope, |
| 2684 | ::tensorflow::Input filenames, |
| 2685 | ::tensorflow::Input compression_type, |
| 2686 | ::tensorflow::Input buffer_size, const |
| 2687 | TextLineDataset::Attrs& attrs) { |
| 2688 | if (!scope.ok()) return; |
| 2689 | auto _filenames = ::tensorflow::ops::AsNodeOut(scope, filenames); |
| 2690 | if (!scope.ok()) return; |
| 2691 | auto _compression_type = ::tensorflow::ops::AsNodeOut(scope, compression_type); |
| 2692 | if (!scope.ok()) return; |
| 2693 | auto _buffer_size = ::tensorflow::ops::AsNodeOut(scope, buffer_size); |
| 2694 | if (!scope.ok()) return; |
| 2695 | ::tensorflow::Node* ret; |
| 2696 | const auto unique_name = scope.GetUniqueNameForOp("TextLineDataset"); |
| 2697 | auto builder = ::tensorflow::NodeBuilder(unique_name, "TextLineDataset") |
| 2698 | .Input(_filenames) |
| 2699 | .Input(_compression_type) |
| 2700 | .Input(_buffer_size) |
| 2701 | .Attr("metadata", attrs.metadata_) |
| 2702 | ; |
| 2703 | scope.UpdateBuilder(&builder); |
| 2704 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2705 | if (!scope.ok()) return; |
| 2706 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2707 | this->operation = Operation(ret); |
| 2708 | this->handle = Output(ret, 0); |
| 2709 | } |
| 2710 | |
| 2711 | TextLineDataset::TextLineDataset(const ::tensorflow::Scope& scope, |
| 2712 | ::tensorflow::Input filenames, |
| 2713 | ::tensorflow::Input compression_type, |
| 2714 | ::tensorflow::Input buffer_size) |
| 2715 | : TextLineDataset(scope, filenames, compression_type, buffer_size, TextLineDataset::Attrs()) {} |
| 2716 | |
| 2717 | UnwrapDatasetVariant::UnwrapDatasetVariant(const ::tensorflow::Scope& scope, |
| 2718 | ::tensorflow::Input input_handle) { |
| 2719 | if (!scope.ok()) return; |
| 2720 | auto _input_handle = ::tensorflow::ops::AsNodeOut(scope, input_handle); |
| 2721 | if (!scope.ok()) return; |
| 2722 | ::tensorflow::Node* ret; |
| 2723 | const auto unique_name = scope.GetUniqueNameForOp("UnwrapDatasetVariant"); |
| 2724 | auto builder = ::tensorflow::NodeBuilder(unique_name, "UnwrapDatasetVariant") |
| 2725 | .Input(_input_handle) |
| 2726 | ; |
| 2727 | scope.UpdateBuilder(&builder); |
| 2728 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2729 | if (!scope.ok()) return; |
| 2730 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2731 | this->operation = Operation(ret); |
| 2732 | this->output_handle = Output(ret, 0); |
| 2733 | } |
| 2734 | |
| 2735 | WindowDataset::WindowDataset(const ::tensorflow::Scope& scope, |
| 2736 | ::tensorflow::Input input_dataset, |
| 2737 | ::tensorflow::Input size, ::tensorflow::Input |
| 2738 | shift, ::tensorflow::Input stride, |
| 2739 | ::tensorflow::Input drop_remainder, const |
| 2740 | DataTypeSlice& output_types, const |
| 2741 | gtl::ArraySlice<PartialTensorShape>& |
| 2742 | output_shapes, const WindowDataset::Attrs& attrs) { |
| 2743 | if (!scope.ok()) return; |
| 2744 | auto _input_dataset = ::tensorflow::ops::AsNodeOut(scope, input_dataset); |
| 2745 | if (!scope.ok()) return; |
| 2746 | auto _size = ::tensorflow::ops::AsNodeOut(scope, size); |
| 2747 | if (!scope.ok()) return; |
| 2748 | auto _shift = ::tensorflow::ops::AsNodeOut(scope, shift); |
| 2749 | if (!scope.ok()) return; |
| 2750 | auto _stride = ::tensorflow::ops::AsNodeOut(scope, stride); |
| 2751 | if (!scope.ok()) return; |
| 2752 | auto _drop_remainder = ::tensorflow::ops::AsNodeOut(scope, drop_remainder); |
| 2753 | if (!scope.ok()) return; |
| 2754 | ::tensorflow::Node* ret; |
| 2755 | const auto unique_name = scope.GetUniqueNameForOp("WindowDataset"); |
| 2756 | auto builder = ::tensorflow::NodeBuilder(unique_name, "WindowDataset") |
| 2757 | .Input(_input_dataset) |
| 2758 | .Input(_size) |
| 2759 | .Input(_shift) |
| 2760 | .Input(_stride) |
| 2761 | .Input(_drop_remainder) |
| 2762 | .Attr("output_types", output_types) |
| 2763 | .Attr("output_shapes", output_shapes) |
| 2764 | .Attr("metadata", attrs.metadata_) |
| 2765 | ; |
| 2766 | scope.UpdateBuilder(&builder); |
| 2767 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2768 | if (!scope.ok()) return; |
| 2769 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2770 | this->operation = Operation(ret); |
| 2771 | this->handle = Output(ret, 0); |
| 2772 | } |
| 2773 | |
| 2774 | WindowDataset::WindowDataset(const ::tensorflow::Scope& scope, |
| 2775 | ::tensorflow::Input input_dataset, |
| 2776 | ::tensorflow::Input size, ::tensorflow::Input |
| 2777 | shift, ::tensorflow::Input stride, |
| 2778 | ::tensorflow::Input drop_remainder, const |
| 2779 | DataTypeSlice& output_types, const |
| 2780 | gtl::ArraySlice<PartialTensorShape>& |
| 2781 | output_shapes) |
| 2782 | : WindowDataset(scope, input_dataset, size, shift, stride, drop_remainder, output_types, output_shapes, WindowDataset::Attrs()) {} |
| 2783 | |
| 2784 | WindowOp::WindowOp(const ::tensorflow::Scope& scope, ::tensorflow::InputList |
| 2785 | inputs, const DataTypeSlice& output_types, const |
| 2786 | gtl::ArraySlice<PartialTensorShape>& output_shapes) { |
| 2787 | if (!scope.ok()) return; |
| 2788 | auto _inputs = ::tensorflow::ops::AsNodeOutList(scope, inputs); |
| 2789 | if (!scope.ok()) return; |
| 2790 | ::tensorflow::Node* ret; |
| 2791 | const auto unique_name = scope.GetUniqueNameForOp("WindowOp"); |
| 2792 | auto builder = ::tensorflow::NodeBuilder(unique_name, "WindowOp") |
| 2793 | .Input(_inputs) |
| 2794 | .Attr("output_types", output_types) |
| 2795 | .Attr("output_shapes", output_shapes) |
| 2796 | ; |
| 2797 | scope.UpdateBuilder(&builder); |
| 2798 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2799 | if (!scope.ok()) return; |
| 2800 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2801 | this->operation = Operation(ret); |
| 2802 | this->handle = Output(ret, 0); |
| 2803 | } |
| 2804 | |
| 2805 | WrapDatasetVariant::WrapDatasetVariant(const ::tensorflow::Scope& scope, |
| 2806 | ::tensorflow::Input input_handle) { |
| 2807 | if (!scope.ok()) return; |
| 2808 | auto _input_handle = ::tensorflow::ops::AsNodeOut(scope, input_handle); |
| 2809 | if (!scope.ok()) return; |
| 2810 | ::tensorflow::Node* ret; |
| 2811 | const auto unique_name = scope.GetUniqueNameForOp("WrapDatasetVariant"); |
| 2812 | auto builder = ::tensorflow::NodeBuilder(unique_name, "WrapDatasetVariant") |
| 2813 | .Input(_input_handle) |
| 2814 | ; |
| 2815 | scope.UpdateBuilder(&builder); |
| 2816 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2817 | if (!scope.ok()) return; |
| 2818 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2819 | this->operation = Operation(ret); |
| 2820 | this->output_handle = Output(ret, 0); |
| 2821 | } |
| 2822 | |
| 2823 | ZipDataset::ZipDataset(const ::tensorflow::Scope& scope, |
| 2824 | ::tensorflow::InputList input_datasets, const |
| 2825 | DataTypeSlice& output_types, const |
| 2826 | gtl::ArraySlice<PartialTensorShape>& output_shapes, |
| 2827 | const ZipDataset::Attrs& attrs) { |
| 2828 | if (!scope.ok()) return; |
| 2829 | auto _input_datasets = ::tensorflow::ops::AsNodeOutList(scope, input_datasets); |
| 2830 | if (!scope.ok()) return; |
| 2831 | ::tensorflow::Node* ret; |
| 2832 | const auto unique_name = scope.GetUniqueNameForOp("ZipDataset"); |
| 2833 | auto builder = ::tensorflow::NodeBuilder(unique_name, "ZipDataset") |
| 2834 | .Input(_input_datasets) |
| 2835 | .Attr("output_types", output_types) |
| 2836 | .Attr("output_shapes", output_shapes) |
| 2837 | .Attr("metadata", attrs.metadata_) |
| 2838 | ; |
| 2839 | scope.UpdateBuilder(&builder); |
| 2840 | scope.UpdateStatus(builder.Finalize(scope.graph(), &ret)); |
| 2841 | if (!scope.ok()) return; |
| 2842 | scope.UpdateStatus(scope.DoShapeInference(ret)); |
| 2843 | this->operation = Operation(ret); |
| 2844 | this->handle = Output(ret, 0); |
| 2845 | } |
| 2846 | |
| 2847 | ZipDataset::ZipDataset(const ::tensorflow::Scope& scope, |
| 2848 | ::tensorflow::InputList input_datasets, const |
| 2849 | DataTypeSlice& output_types, const |
| 2850 | gtl::ArraySlice<PartialTensorShape>& output_shapes) |
| 2851 | : ZipDataset(scope, input_datasets, output_types, output_shapes, ZipDataset::Attrs()) {} |
| 2852 | |
| 2853 | } // namespace internal |
| 2854 | } // namespace ops |
| 2855 | } // namespace tensorflow |
| 2856 |
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