* holds the lock. If a hashed slot is empty, and lock is
   * available, a new Cell is created. Otherwise, if the slot
   * exists, a CAS is tried.  Retries proceed by "double hashing",
   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,

   * holds the lock. If a hashed slot is empty, and lock is
   * available, a new Cell is created. Otherwise, if the slot
   * exists, a CAS is tried.  Retries proceed by "double hashing",
   * using a secondary hash (Marsaglia XorShift) to try to find a
   * free slot.
   *
   * The table size is capped because, when there are more threads
   * than CPUs, supposing that each thread were bound to a CPU,

        subproject("build-cache-packaging")
        subproject("build-cache-spi")
        subproject("execution-e2e-tests")
        subproject("file-watching")
        subproject("execution")
        subproject("hashing")
        subproject("persistent-cache")
        subproject("snapshots")
        subproject("worker-main")
        subproject("workers")
    }
}

// Documentation Module
module("documentation") {

            moduleRegistry.getModule("gradle-file-temp").getImplementationClasspath().getAsURLs(),
            moduleRegistry.getModule("gradle-hashing").getImplementationClasspath().getAsURLs(),
            moduleRegistry.getModule("gradle-base-services").getImplementationClasspath().getAsURLs(),

    auto input_ty = mlir::cast<ShapedType>(input.getType());
    auto min_ty = mlir::cast<ShapedType>(min.getType());
    auto max_ty = mlir::cast<ShapedType>(max.getType());

    if (!input_ty.hasRank() || !min_ty.hasRank() || !max_ty.hasRank()) {
      return failure();
    }

    auto shape = rewriter.create<TF::ShapeOp>(
        op.getLoc(),
        tensorflow::GetTypeFromTFTensorShape({input_ty.getRank()},

  std::vector<int32_t> shape;
  if (tensor_type.hasRank()) {
    llvm::ArrayRef<int64_t> shape_ref = tensor_type.getShape();
    shape = std::vector<int32_t>(shape_ref.begin(), shape_ref.end());
  }

  variant_params.push_back(
      tflite::CreateVariantSubType(builder_, builder_.CreateVector(shape),
                                   tflite_element_type, tensor_type.hasRank()));
  return variant_params;
}

import static org.gradle.integtests.fixtures.executer.OutputScrapingExecutionResult.normalizeLambdaIds;
import static org.gradle.internal.hash.Hashing.hashString;
import static org.gradle.util.Matchers.normalizedLineSeparators;
import static org.hamcrest.CoreMatchers.containsString;
import static org.hamcrest.CoreMatchers.equalTo;

    // Here we assume that the element_shape won't be changed before calling
    // the first `TensorListSetItemOp`.
    if (auto shaped_type = element_shape.getType().dyn_cast<ShapedType>()) {
      if (shaped_type.hasRank() && shaped_type.getRank() == 0) {
        bool element_shape_acquired = false;
        auto uses = op.getResult().getUses();
        for (auto &use : llvm::make_early_inc_range(uses)) {

- Hashing of KeyID in Logs
  

        multi-hot, or tf-idf encoded representations
    *   `CategoryCrossing`: create new categorical features representing
        co-occurrences of previous categorical feature values
    *   `Hashing`: the hashing trick, for large-vocabulary categorical features
    *   `Discretization`: turn continuous numerical features into categorical
        features by binning their values