op.getLoc(),
        DenseStringElementsAttr::get(
            RankedTensorType::get(static_cast<int64_t>(lines.size()),
                                  StringType::get(rewriter.getContext())),
            lines));

    Value value_constant_tensor = rewriter.create<arith::ConstantOp>(
        op.getLoc(), rewriter.getI64TensorAttr(line_nums));

 */
package org.codelibs.fess.crawler.entity;

import java.io.Serializable;

/**
 * @author shinsuke
 *
 */
public interface Sitemap extends Serializable {

    String getLoc();

    String getLastmod();

std::pair<std::string, std::string> GetFuncNameAndNodeName(
    TF::PartitionedCallOp call_op, const FlatSymbolRefAttr &f_attr) {
  std::optional<QuantizationUnitLoc::QuantizationUnit> quant_unit =
      FindQuantizationUnitFromLoc(call_op->getLoc());
  return std::make_pair(quant_unit->func_name(), quant_unit->node_name());
}

std::pair<std::string, std::string> GetFuncNameAndNodeName(
    TF::XlaCallModuleOp call_op, const FlatSymbolRefAttr &f_attr) {

  OpBuilder builder(&module->getBodyRegion());
  context.loadDialect<TF::TensorFlowDialect>();

  Value value = CreateConstValue<int8_t>(builder, module->getLoc(),
                                         unpacked_shape, unpacked_values);
  Value packed_value = PackOperand(builder, module->getLoc(), value, pack_dim);
  DenseIntElementsAttr packed_value_attr;
  ASSERT_TRUE(matchPattern(packed_value, m_Constant(&packed_value_attr)));

        cluster_func->getLoc(), partitioned_output_types, result,
        partitioned_input.getPartitionDimsAttr(),
        partitioned_input.get_XlaShardingAttr());
    for (auto [i, value] : llvm::enumerate(partitioned_output.getOutput())) {
      const auto& resource = packed_input ? inputs[0] : inputs[i];
      builder.create<TF::AssignVariableOp>(
          assign_var->getLoc(), /*resource=*/resource, /*value=*/value);

        maxs.push_back(rmax);
      }
      quant_type = quantfork::fakeQuantAttrsToType(
          op.getLoc(), num_bits, *op.getAxis(), mins, maxs, narrow_range,
          expressed, is_signed);
      if (legacy_float_scale) {
        quant_type = DownCastScale(quant_type, mins, maxs, op->getLoc());
      }
    } else if (auto stats =
                   op.getLayerStats().dyn_cast<DenseFPElementsAttr>()) {

        mlir::isa<quant::QuantizedType>(element_type)) {
      return;
    }
    op_builder->setInsertionPoint(const_op);
    Value scalar = op_builder->create<mhlo::ConstantOp>(
        const_op->getLoc(),
        DenseElementsAttr::get(
            RankedTensorType::get(/*shape=*/{}, element_type),
            splat_elements_attr.getSplatValue<Attribute>()));
    auto broadcast_dims = DenseIntElementsAttr::get(

      ShapedType shaped_type =
          RankedTensorType::get({1}, TF::StringType::get(builder.getContext()));
      auto const_op = builder.create<TF::ConstOp>(
          asset.getLoc(),
          DenseStringElementsAttr::get(shaped_type, {filename}));
      for (auto init_op : init_table_from_text_file_ops_to_erase) {
        // Replace the InitializeTableFromTextFileV2Op to use the saved model's

        }
        return deploymentDescriptor;
    }

    /**
     * A location for dependency libraries to include in the 'lib' directory of the EAR archive.
     */
    @Internal
    public CopySpec getLib() {
        return ((CopySpecInternal) lib).addChild();
    }

    /**
     * Adds dependency libraries to include in the 'lib' directory of the EAR archive.
     *

      rewriter.setInsertionPointAfter(op);
      // create new F16 constant op in that location
      ConstantOp new_const = rewriter.create<ConstantOp>(
          op->getLoc(), new_result_type, new_value_attr);
      ConvertOp dcast =
          rewriter.create<ConvertOp>(op->getLoc(), old_result_type, new_const);
      // replace all convert ops with dq op.
      convert_op->replaceAllUsesWith(dcast);