bts, err = msgp.Skip(bts)
			if err != nil {
				err = msgp.WrapError(err)
				return
			}
		}
	}
	o = bts
	return
}

// Msgsize returns an upper bound estimate of the number of bytes occupied by the serialized message
func (z *healingTracker) Msgsize() (s int) {

    // Attribute for that proto, and finally splat the Attribute.

    TensorProto tensor_copy = input_tensor;
    auto* shape = tensor_copy.mutable_tensor_shape();
    shape->clear_dim();
    shape->add_dim()->set_size(1);

    TF_ASSIGN_OR_RETURN(ElementsAttr single_attr,
                        ConvertTensorProto(tensor_copy, builder));

    llvm::SmallVector<int64_t> original_dimensions;

    auto dy_data_flat = dy_datas[y_idx].flat<Y_T>();
    const int64_t dy_size = y_shapes[y_idx].num_elements();

    // Compute the theoretical Jacobians one row at a time by back propagating
    // '1.0' (or '1' and 'i' if y is complex) for each element of 'dy', while
    // holding all other elements of 'dy' at zero.
    for (int c = 0; c < dy_size; ++c) {
      int unit_dimension = 0;

        try {
            SearchResponse response = client.prepareSearch().setIndices(actualIndex).setScroll(settings.getScrollTimeout())
                    .setQuery(QueryBuilders.termQuery(FieldNames.ARRAY_KEY, key)).setSize(500).execute()
                    .actionGet(settings.getSearchTimeout());
            String scrollId = response.getScrollId();

  // creating the Attribute. If it tries to, we'll crash OOM here.
  TensorProto tensor;
  tensor.set_dtype(DT_FLOAT);
  tensor.mutable_tensor_shape()->add_dim()->set_size(1ULL << 35);
  tensor.add_float_val(42.0);

  mlir::MLIRContext context;
  mlir::Builder builder(&context);
  TF_ASSERT_OK_AND_ASSIGN(mlir::ElementsAttr attribute,

            final QueryBuilder queryBuilder) {
        try {
            SearchResponse response = client.prepareSearch(index).setQuery(queryBuilder).setSize(500).setScroll(settings.getScrollTimeout())
                    .execute().actionGet(settings.getSearchTimeout());
            String scrollId = response.getScrollId();
            try {

				sz = listSize.Multiply(elemSize)
			}

			if len(args) > 0 {
				sepSize := l.sizeEstimate(args[0])
				minSeparators := uint64(0)
				maxSeparators := uint64(0)
				if listSize.Min > 0 {
					minSeparators = listSize.Min - 1
				}
				if listSize.Max > 0 {
					maxSeparators = listSize.Max - 1
				}
				sz = sz.Add(sepSize.Multiply(checker.SizeEstimate{Min: minSeparators, Max: maxSeparators}))
			}

  for (const std::optional<llvm::ArrayRef<int64_t>>& shape : shapes) {
    TensorShapeProto& tshape = *shape_list.add_shape();
    if (shape.has_value()) {
      for (int64_t dim : *shape) {
        tshape.add_dim()->set_size(mlir::ShapedType::isDynamic(dim) ? -1 : dim);
      }
    } else {
      tshape.set_unknown_rank(true);
    }
  }

  // If shape is already set, override it. This can happen if we import

	if payload != nil {
		if sz := payload.Msgsize(); cap(msg.Payload) < sz {
			PutByteBuffer(msg.Payload)
			msg.Payload = GetByteBufferCap(sz)
		}
		var err error
		msg.Payload, err = payload.MarshalMsg(msg.Payload[:0])
		msg.Op = payload.Op()
		if err != nil {
			return err
		}
	}
	defer PutByteBuffer(msg.Payload)
	dst := GetByteBufferCap(msg.Msgsize())
	dst, err := msg.MarshalMsg(dst)
	if err != nil {

	SYS_SOCKET               = 394 // { int|sys|30|socket(int domain, int type, int protocol); }
	SYS_GETFH                = 395 // { int|sys|30|getfh(const char *fname, void *fhp, size_t *fh_size); }
	SYS_MOUNT                = 410 // { int|sys|50|mount(const char *type, const char *path, int flags, void *data, size_t data_len); }