* governing permissions and limitations under the License.
 */
package org.codelibs.fess.crawler.entity;

/**
 * @author shinsuke
 *
 */
public interface AccessResultData<IDTYPE> {

    IDTYPE getId();

    void setId(IDTYPE id);

    String getTransformerName();

    void setTransformerName(String transformerName);

    byte[] getData();

    String getDataAsString();

     * @param str
     *            文字列。{@literal null}であってはいけません
     * @param ctype
     *            文字のタイプの配列。{@literal null}や空配列であってはいけません
     */
    public Tokenizer(final String str, final byte[] ctype) {
        assertArgumentNotNull("str", str);
        assertArgumentNotEmpty("ctype", ctype);

        this.str = str;
        this.ctype = ctype;
    }

    /**
     * @param ctype2
     *            文字のタイプの配列

          }
        }
      }
    }
  }
}
node {
  name: "Const0"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT64
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT64
        tensor_shape {
        }
        int64_val: -1
      }
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {

class Variable {
 public:
  // Construct a Variable from a resource-dtype TFE_TensorHandle and an
  // indication of the dtype of the variable's value.
  //
  // Note that creating this resource-dtype handle can fail, so `Create` is a
  // separate static method which returns a status.
  Variable(TFE_TensorHandle* handle, TF_DataType type)
      : handle_(handle), type_(type) {}

  // functionality will be supported in the future.

  // Create a host tensor and copy data into it.
  //
  // Raises an error if shape or dtype are incompatible with T.
  // TODO(b/189458441): Update this when we decide on the representation of
  // shape and dtype in this API.
  // Disclaimer: This API is subject to change as we add support for creating
  // device tensors b/187222691 and enable buffer re-use b/187223179.

namespace tf {
namespace libtf {
namespace impl {
/// @brief The TensorSpec struct.
///
/// The TensorSpec describes the shape and dtype of a Tensor.

struct TensorSpec {
  tensorflow::PartialTensorShape shape;
  tensorflow::DataType dtype;

  bool operator==(const TensorSpec& o) const {
    return dtype == o.dtype && shape.IsIdenticalTo(o.shape);
  }

  /// Overload AbslHashValue to make TensorSpec hashable.
  template <typename H>

namespace tensorflow {

Variable::Variable(ImmediateExecutionContext* ctx, DataType dtype,
                   TensorShape shape, absl::optional<std::string> name,
                   ImmediateTensorHandlePtr handle)
    : TensorHandleConvertible(std::move(handle)),
      name_(name.has_value() ? *name : "Variable"),
      dtype_(dtype),
      shape_(shape),
      ctx_(ctx) {}

Variable::~Variable() {

package os

import (
	"syscall"
	"unsafe"
)

const _P_PID = 0

func wait6(idtype, id, options int) (status int, errno syscall.Errno) {
	// freebsd32_wait6_args{ idtype, id1, id2, status, options, wrusage, info }
	_, _, errno = syscall.Syscall9(syscall.SYS_WAIT6, uintptr(idtype), uintptr(id), 0, uintptr(unsafe.Pointer(&status)), uintptr(options), 0, 0, 0, 0)
	return status, errno

	if tid == 0 {
		return nil, plainError("missing tid")
	}

	f := efaceOf(&fn)
	if f._type == nil || f._type.Kind_&abi.KindMask != abi.Func {
		return nil, plainError("fn must be a function")
	}
	fv := (*funcval)(f.data)

	a := efaceOf(&stackArgs)
	if a._type != nil && a._type.Kind_&abi.KindMask != abi.Pointer {
		return nil, plainError("args must be a pointer or nil")
	}
	argp := a.data

  %dense_default_0 = "tf.Const"() {device = "/device:CPU:0", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
  %dense_default_1 = "tf.Const"() {device = "/device:CPU:0", dtype = f32, value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>
  %dense_keys = "tf.Const"() {device = "/device:CPU:0", dtype = !tf_type.string, value = dense<""> : tensor<2x!tf_type.string>} : () -> tensor<2x!tf_type.string>