Method <org.gradle.api.artifacts.repositories.FlatDirectoryArtifactRepository.getDirs()> does not have raw return type assignable to org.gradle.api.provider.Property in (FlatDirectoryArtifactRepository.java:0)

      is_scalar_rhs = true;
    } else if (constant_val_type.getRank() > 1) {
      return failure();
    }

    Value filter = fc_op.getFilter();
    Value bias = fc_op.getBias();
    ElementsAttr bias_value;
    const bool is_none_bias = mlir::isa<NoneType>(bias.getType());
    if (fc_op.getFusedActivationFunction() != "NONE") return failure();

------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Alert {\n  constructor(element) {\n    this._element = element\n  }\n\n  // Getters\n\n  static get VERSION() {\n    return VERSION\n  }\n\n  // Public\n\n  close(element) {\n    let rootElement = this._element\n    if (element) {\n      rootElement = this._getRootElement(element)\n    }\n\n    const customEvent = this._triggerC...

------------------------------------------------------------------------\n * Class Definition\n * ------------------------------------------------------------------------\n */\n\nclass Alert {\n  constructor(element) {\n    this._element = element\n  }\n\n  // Getters\n\n  static get VERSION() {\n    return VERSION\n  }\n\n  // Public\n\n  close(element) {\n    let rootElement = this._element\n    if (element) {\n      rootElement = this._getRootElement(element)\n    }\n\n    const customEvent = this._triggerC...

  // Bias tensor is optional.
  const bool has_bias = !(!getBias() || getBias().getType().isa<NoneType>());

  // Get the tensors.
  DenseElementsAttr input_tensor, weights_tensor, bias_tensor;
  if (!matchPattern(getInput(), m_Constant(&input_tensor)) ||
      !matchPattern(getFilter(), m_Constant(&weights_tensor)) ||
      (has_bias && !matchPattern(getBias(), m_Constant(&bias_tensor)))) {
    return failure();
  }

          "format");
  }

  if (!IsOfRankOrUnranked(op.getBias(), 1))
    return op.emitOpError("requires bias operand to have rank exactly one");

  RankedTensorType value_ty =
      mlir::dyn_cast<RankedTensorType>(op.getValue().getType());
  RankedTensorType bias_ty =
      mlir::dyn_cast<RankedTensorType>(op.getBias().getType());
  if (!bias_ty || !value_ty) return success();

            }
        }

        /**
         * Generates the init method with deprecation logging disabled.
         *
         * This is necessary because the initialization work invokes property getters on the decorated instance.
         */
        private void generateInitMethod() {

            // private void $gradleInit() { DeprecationLogger.whileDisabled(this::$gradleInitWork) }

        if (weight > 0) {
          return e;
        }
      }
      throw new AssertionError();
    }

    /** Returns first entry of bin for given hash. */
    ReferenceEntry<K, V> getFirst(int hash) {
      // read this volatile field only once
      AtomicReferenceArray<ReferenceEntry<K, V>> table = this.table;
      return table.get(hash & (table.length() - 1));
    }

        if (weight > 0) {
          return e;
        }
      }
      throw new AssertionError();
    }

    /** Returns first entry of bin for given hash. */
    ReferenceEntry<K, V> getFirst(int hash) {
      // read this volatile field only once
      AtomicReferenceArray<ReferenceEntry<K, V>> table = this.table;
      return table.get(hash & (table.length() - 1));
    }

    ShapedTypeComponents inferred = std::get<1>(result);
    TensorType inferred_type;
    if (inferred.hasRank()) {
      inferred_type =
          RankedTensorType::get(inferred.getDims(), inferred.getElementType());

    } else {
      inferred_type = UnrankedTensorType::get(inferred.getElementType());
    }
    inferred_type =