# (1)
FROM python:3.9 as requirements-stage

# (2)
WORKDIR /tmp

# (3)
RUN pip install poetry

# (4)
COPY ./pyproject.toml ./poetry.lock* /tmp/

# (5)
RUN poetry export -f requirements.txt --output requirements.txt --without-hashes

# (6)
FROM python:3.9

# (7)
WORKDIR /code

# (8)
COPY --from=requirements-stage /tmp/requirements.txt /code/requirements.txt

		// call will report top level prefixes in deleted state, whereas spark/hadoop interpret this as non-empty
		// and throw a 404 exception. This is especially a problem for spark jobs overwriting the same partition
		// repeatedly. This workaround recursively lists the top 3 entries including delete markers to reflect the
		// correct state of the directory in the list results.

     * value}
     */
    void setValue(S segment, E entry, V value);
  }

  /**
   * An entry in a hash table of a {@link Segment}.
   *
   * <p>Entries in the map can be in the following states:
   *
   * <p>Valid: - Live: valid key/value are set
   *
   * <p>Invalid: - Collected: key/value was partially collected, but not yet cleaned up
   */

	s.fsType = info.FSType

	if rootDrive {
		return s, errDriveIsRoot
	}

	// Sanitize before setting it
	if info.NRRequests > 0 {
		s.nrRequests = info.NRRequests
	}

	// We stagger listings only on HDDs.
	if info.Rotational == nil || *info.Rotational {
		s.rotational = true
		s.walkMu = &sync.Mutex{}
		s.walkReadMu = &sync.Mutex{}
	}

	if cleanUp {