out = math_ops.select(condition, out, ones)
        elif self.same_scale_op == 'slice':
          begin = array_ops.zeros((array_ops.rank(out)), dtype=dtypes.int32)
          size = array_ops.ones((array_ops.rank(out)), dtype=dtypes.int32)
          out = array_ops.slice(out, begin, size)
        elif self.same_scale_op == 'transpose':
          out = array_ops.transpose(out)
        else:

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

/**
 * @author shinsuke
 *
 */
public class UrlQueueImpl<IDTYPE> implements UrlQueue<IDTYPE> {
    protected IDTYPE id;

    protected String sessionId;

    protected String method;

    protected String url;

    protected String metaData;

    protected String encoding;

// CHECK-NEXT:       attr {
// CHECK:              key: "dtype"
// CHECK-NEXT:         value {
// CHECK-NEXT:           type: DT_INT32
// CHECK-NEXT:         }
// CHECK-NEXT:       }
// CHECK-NEXT:       attr {
// CHECK-NEXT:         key: "value"
// CHECK-NEXT:         value {
// CHECK-NEXT:           tensor {
// CHECK-NEXT:             dtype: DT_INT32
// CHECK-NEXT:             tensor_shape {

  switch (dtype) {
#define CASE(type)                                       \
  case DataTypeToEnum<type>::value: {                    \
    type* typed_a = static_cast<type*>(a);               \
    type* typed_b = static_cast<type*>(b);               \
    for (int64_t i = 0; i < num_elements; ++i) {         \
      if (DataTypeIsFloating(dtype)) {                   \
        EXPECT_FLOAT_EQ(static_cast<float>(typed_a[i]),  \

    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo{{_.*_1}}"
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<1> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo1"

  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_INT32
        tensor_shape {
        }
        int_val: 0
      }
    }
  }
}
node {
  name: "Case/input_0"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_FLOAT
    }
  }
  attr {

#include "tsl/platform/statusor.h"

namespace tensorflow {

static size_t GetTensorSize(const TensorShape& shape, const DataType dtype) {
  return shape.num_elements() * DataTypeSize(dtype);
}

absl::StatusOr<Tensor> MakeTensorFromPjRtBuffer(
    const DataType dtype, const TensorShape& shape,
    std::unique_ptr<xla::PjRtBuffer> pjrt_buffer) {
  TF_ASSIGN_OR_RETURN(std::unique_ptr<xla::PjRtBuffer::ExternalReference> ref,

    value {
      tensor {
        dtype: DT_INT64
        tensor_shape {
        }
        int64_val: 5
      }
    }
  }
}
node {
  name: "drop_remainder"
  op: "Const"
  attr {
    key: "dtype"
    value {
      type: DT_BOOL
    }
  }
  attr {
    key: "value"
    value {
      tensor {
        dtype: DT_BOOL
        tensor_shape {
        }

    key: "dtype"
    value {
      type: DT_INT32
    }
  }
  attr {
    key: "shape"
    value {
      shape {
        unknown_rank: true
      }
    }
  }
  attr {
    key: "type"
    value {
      type: DT_INT32
    }
  }
  experimental_debug_info {
  }
}
node {
  name: "static"
  op: "PlaceholderWithDefault"
  input: "tf.Const1"
  attr {
    key: "dtype"
    value {

        VLOG(1) << "[potentially conservative] Op type `" << node.type_string()
                << "` is stateful but effects not modelled";
      } else {
        // See if any resource type is used.
        bool resource = false;
        std::function<bool(mlir::Type)> record_resource;
        record_resource = [&](mlir::Type type) {
          type.walk([&](mlir::Type t) {