} else {
			ld.AddGotSym(target, ldr, syms, targ, 0)
		}
		// turn into two relocations, one for each instruction.
		su := ldr.MakeSymbolUpdater(s)
		r.SetType(objabi.R_ARM64_GOT)
		r.SetSiz(4)
		r.SetSym(syms.GOT)
		r.SetAdd(int64(ldr.SymGot(targ)))
		r2, _ := su.AddRel(objabi.R_ARM64_GOT)
		r2.SetSiz(4)
		r2.SetOff(r.Off() + 4)
		r2.SetSym(syms.GOT)

    RankedTensorType act_type = mlir::dyn_cast<RankedTensorType>(act.getType());
    if (!act_type) return failure();
    Type act_ele_type = act_type.getElementType();
    // To support mixed precision, the statistics type, which maybe more
    // precise than the input types, are used for this op.
    Type kernel_type = mlir::cast<TensorType>(scale.getType()).getElementType();

func (rel Reloc) Weak() bool                 { return objabi.RelocType(rel.Reloc.Type())&objabi.R_WEAK != 0 }
func (rel Reloc) SetType(t objabi.RelocType) { rel.Reloc.SetType(uint16(t)) }
func (rel Reloc) Sym() Sym                   { return rel.l.resolve(rel.r, rel.Reloc.Sym()) }
func (rel Reloc) SetSym(s Sym)               { rel.Reloc.SetSym(goobj.SymRef{PkgIdx: 0, SymIdx: uint32(s)}) }

        loc, tensorflow::GetTypeFromTFTensorShape({-1}, shape_dtype),
        input_handle);

    Type branch_args_type[] = {input_handle.getType(), input_shape.getType(),
                               size_diff.getType(), size.getType()};
    Type branch_result_type[] = {result_type};
    auto func_type = FunctionType::get(rewriter.getContext(), branch_args_type,

    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());

	public final fun addPart (Lokhttp3/RequestBody;)Lokhttp3/MultipartBody$Builder;
	public final fun build ()Lokhttp3/MultipartBody;
	public final fun setType (Lokhttp3/MediaType;)Lokhttp3/MultipartBody$Builder;
}

public final class okhttp3/MultipartBody$Companion {
}

public final class okhttp3/MultipartBody$Part {

    }
  }

  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

    for (const auto& p : *shapes_and_types) {
      auto* out_shape_and_type = handle_data.add_shape_and_type();
      ic->ShapeHandleToProto(p.shape, out_shape_and_type->mutable_shape());
      out_shape_and_type->set_dtype(p.dtype);
      *out_shape_and_type->mutable_type() = p.type;
    }
  }
  string str_data;
  handle_data.SerializeToString(&str_data);

  if (opts.HaveError()) return nullptr;
  NodeBuilder node_builder(opts.GetNameForOp("Const"), "Const",
                           opts.op_registry());
  TensorProto value;
  value.set_dtype(dtype);
  for (int dim : shape) {
    value.mutable_tensor_shape()->add_dim()->set_size(dim);
  }
  return opts.WithAttr("value", value)
      .WithAttr("dtype", dtype)
      .FinalizeBuilder(&node_builder);
}

  enum FlatBuffersVTableOffset FLATBUFFERS_VTABLE_UNDERLYING_TYPE {
    VT_TYPE = 4
  };
  tflite::LSHProjectionType type() const {
    return static_cast<tflite::LSHProjectionType>(GetField<int8_t>(VT_TYPE, 0));
  }
  bool Verify(::flatbuffers::Verifier &verifier) const {
    return VerifyTableStart(verifier) &&
           VerifyField<int8_t>(verifier, VT_TYPE, 1) &&
           verifier.EndTable();
  }