fld->ReplaceFunction(shape_inference_graph_name, fdef_replace));

  return absl::OkStatus();
}

void SetMaximalSharding(NodeDefBuilder& node_builder) {
  xla::OpSharding sharding;
  sharding.set_type(xla::OpSharding::MAXIMAL);
  sharding.add_tile_assignment_dimensions(1);
  sharding.add_tile_assignment_devices(0);
  node_builder.Attr("_XlaSharding", sharding.SerializeAsString());
}

  }
  if (refined_shape || lhs_element_type != lhs_shape_type.getElementType()) {
    Type new_type;
    if (!lhs_shape_type.hasRank() && !rhs_shape_type.hasRank())
      new_type = UnrankedTensorType::get(lhs_element_type);
    else
      new_type = lhs_shape_type.clone(shape, lhs_element_type);
    DCOMMENT("Refined to: " << new_type);
    return new_type;
  }
  DCOMMENT("No refinement " << lhs);
  return lhs;
}

	types.SetType(sym.STYPE)
	types.SetSize(8)
	ldr.SetAttrSpecial(types.Sym(), false)

	etypes := ldr.CreateSymForUpdate("runtime.etypes", 0)
	etypes.SetType(sym.SFUNCTAB)
	ldr.SetAttrSpecial(etypes.Sym(), false)

	if ctxt.HeadType == objabi.Haix {
		rodata := ldr.CreateSymForUpdate("runtime.rodata", 0)
		rodata.SetType(sym.SSTRING)
		rodata.SetSize(8)

						IP:       "100.101.102.103",
						Port:     3001,
						Protocol: strings.ToLower(string(v1.ProtocolSCTP)),
						SetType:  utilipset.HashIPPort,
					},
					{
						IP:       "100.101.102.104",
						Port:     3001,
						Protocol: strings.ToLower(string(v1.ProtocolSCTP)),
						SetType:  utilipset.HashIPPort,
					},
					{
						IP:       "100.101.102.105",
						Port:     3001,

  }

  if (auto int_type = mlir::dyn_cast<IntegerType>(etype)) {
    auto out_type = tensorflow::GetTypeFromTFTensorShape(out_shape, etype);
    APInt val(int_type.getWidth(), 0, int_type.getSignedness());
    return DenseElementsAttr::get(out_type, val);
  }

  return {};
}

//===----------------------------------------------------------------------===//
// EmptyTensorListOp

            getType(Project.class),
            getType("Lorg/gradle/api/internal/project/ProjectInternal;"),
            getType(Gradle.class),
            getType("Lorg/gradle/api/internal/GradleInternal;")
        );
        private static final Type JAVA_LANG_REFLECT_TYPE = getType(java.lang.reflect.Type.class);
        private static final Type OBJECT_TYPE = getType(Object.class);

          mlir::tf_saved_model::GlobalTensorOp>(func, i, symbol_table);
      if (global_tensor) {
        auto old_type = arg.getType();
        auto new_type =
            mlir::tf_saved_model::GetBoundInputArgTypeFor(global_tensor);
        arg.setType(new_type);
        if (global_tensor.getIsMutable()) {
          auto arg_with_original_type = builder.create<mlir::TF::CastOp>(

    auto new_cst = cst.reshape(RankedTensorType::get(
        normalized_shape, cst.getType().getElementType()));
    Type new_type = new_cst.getType();
    if (!IsBroadcastableElementsAttrAndType(new_type, filter.getType())) {
      return failure();
    }

    auto new_op =
        rewriter.create<arith::ConstantOp>(mul_op.getLoc(), new_type, new_cst);
    Value new_const_val = new_op.getResult();

    if (!mlir::cast<ShapedType>(conv_op.getRhs().getType()).hasStaticShape()) {
      return false;
    }
    if (!mlir::cast<ShapedType>(conv_op.getLhs().getType()).hasStaticShape() &&
        !mlir::cast<ShapedType>(conv_op.getType()).hasStaticShape()) {
      auto dnums = conv_op.getDimensionNumbers();
      auto lhs_type = mlir::cast<ShapedType>(conv_op.getLhs().getType());
      auto out_type = mlir::cast<ShapedType>(conv_op.getType());

    }
  }

  std::vector<int64_t> new_shape = value_attr.getType().getShape().vec();
  new_shape[0] = lookup_attr.getType().getShape()[0];
  auto new_type = value_attr.getType().clone(new_shape);

  return DenseElementsAttr::get(new_type, new_values);
}

//===----------------------------------------------------------------------===//
// ConstBytesAttr