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 *PartialOperation) Msgsize() (s int) {

			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 *WalkDirOptions) Msgsize() (s int) {

			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 *SiteResyncStatus) Msgsize() (s int) {
	s = 1 + 2 + msgp.IntSize + 3 + z.Status.Msgsize() + 4 + msgp.StringPrefixSize + len(z.DeplID) + 5 + msgp.MapHeaderSize
	if z.BucketStatuses != nil {

  virtual absl::Status AddDevices(
      std::vector<std::unique_ptr<Device>> devices) = 0;

  // Block until all pending nodes are finished.
  virtual absl::Status AsyncWait() = 0;

  // Add a function (serialized FunctionDef protocol buffer) so that it can
  // be executed as an op. Return error if the function with the same name
  // already exists.
  virtual absl::Status AddFunctionDef(const FunctionDef& fdef) = 0;

come with the compiler are not designed to be used with untrusted data and
should be used with caution when working with untrusted models.

### Saved graphs and checkpoints

When loading untrusted serialized computation graphs (in form of a `GraphDef`,
`SavedModel`, or equivalent on-disk format), the set of computation primitives
available to TensorFlow is powerful enough that you should assume that the

import static com.google.common.collect.Maps.immutableEntry;
import static com.google.common.collect.ReflectionFreeAssertThrows.assertThrows;
import static com.google.common.testing.SerializableTester.reserialize;
import static com.google.common.truth.Truth.assertThat;
import static com.google.common.truth.Truth.assertWithMessage;
import static java.util.Arrays.asList;
import static java.util.Collections.singletonMap;

            model = new Model(modelReader.read(sReader));
        } catch (XMLStreamException e) {
            throw new ModelInterpolationException(
                    "Cannot read project model from interpolating filter of serialized version.", e);
        }

        return model;
    }

    /**
     * Interpolates all expressions in the src parameter.
     * <p>
     * The algorithm used for each expression is:
     * <ul>

            This is mainly a hack for compatibility with SQLAlchemy objects, they
            store internal SQLAlchemy-specific state in attributes named with `_sa`,
            and those objects can't (and shouldn't be) serialized to JSON.
            """
        ),
    ] = True,
) -> Any:
    """
    Convert any object to something that can be encoded in JSON.

    // Write out all elements in the proper order.
    for (int i = 0; i < length; i++) {
      s.writeDouble(get(i));
    }
  }

  /** Reconstitutes the instance from a stream (that is, deserializes it). */
  private void readObject(ObjectInputStream s) throws IOException, ClassNotFoundException {
    s.defaultReadObject();

    int length = s.readInt();

/**
 * Collections of strategies of generating the k * log(M) bits required for an element to be mapped
 * to a BloomFilter of M bits and k hash functions. These strategies are part of the serialized form
 * of the Bloom filters that use them, thus they must be preserved as is (no updates allowed, only
 * introduction of new versions).
 *