justify-content: center;
    padding: 3rem 2rem;
    text-align: center;
    height: 100%;
    background: linear-gradient(180deg, #fafbfc 0%, #ffffff 100%);
}

.empty-state-icon {
    width: 80px;
    height: 80px;
    border-radius: 50%;
    background: linear-gradient(135deg, #0052cc 0%, #2684ff 100%);
    display: flex;
    align-items: center;
    justify-content: center;

 * described by <a
 * href="http://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population">
 * wikipedia</a> as providing "Linear interpolation of the modes for the order statistics for the
 * uniform distribution on [0,1]."
 *
 * <h3>Handling of non-finite values</h3>
 *

   * equivalently, if this inverse view contains a key that is equal to {@code value}).
   *
   * <p>Due to the property that values in a BiMap are unique, this will tend to execute in
   * faster-than-linear time.
   *
   * @param value the object to search for in the values of this BiMap
   * @return true if a mapping exists from a key to the specified value
   */
  @Override

    It significantly reduces the peak memory required and latency incurred
    during restore.
*   Added a new library for library of matrix-free (iterative) solvers for
    linear equations, linear least-squares, eigenvalues and singular values in
    tensorflow/contrib/solvers. Initial version has lanczos bidiagonalization,
    conjugate gradients and CGLS.

 *       {@link #poll} and {@link #remove()}) run in {@code O(log n) time}.
 *   <li>The {@link #remove(Object)} and {@link #contains} operations require linear ({@code O(n)})
 *       time.
 *   <li>If you only access one end of the queue, and don't use a maximum size, this class is
 *       functionally equivalent to {@link PriorityQueue}, but significantly slower.
 * </ul>
 *

   * equivalently, if this inverse view contains a key that is equal to {@code value}).
   *
   * <p>Due to the property that values in a BiMap are unique, this will tend to execute in
   * faster-than-linear time.
   *
   * @param value the object to search for in the values of this BiMap
   * @return true if a mapping exists from a key to the specified value
   */
  @Override

 *       {@link #poll} and {@link #remove()}) run in {@code O(log n) time}.
 *   <li>The {@link #remove(Object)} and {@link #contains} operations require linear ({@code O(n)})
 *       time.
 *   <li>If you only access one end of the queue, and don't use a maximum size, this class is
 *       functionally equivalent to {@link PriorityQueue}, but significantly slower.
 * </ul>
 *

/// info | Información

El punto importante es que tu app podrá producir cada línea a su turno, mientras el cliente consume las líneas anteriores.

///

/// note | Detalles técnicos

def extract_code_includes(lines: list[str]) -> list[CodeIncludeInfo]:
    """
    Extract lines that contain code includes.

    Return list of CodeIncludeInfo, where each dict contains:
    - `line_no` - line number (1-based)
    - `line` - text of the line
    """

    includes: list[CodeIncludeInfo] = []
    for line_no, line in enumerate(lines, start=1):
        if CODE_INCLUDE_RE.match(line):

executado antes de a aplicação **começar** a receber requisições e logo depois que ela **termina** de lidar com as requisições, ele cobre todo o **lifespan** da aplicação (a palavra "lifespan" será importante em um segundo 😉).

Isso pode ser muito útil para configurar **recursos** que você precisa usar por toda a aplicação, e que são **compartilhados** entre as requisições e/ou que você precisa **limpar** depois. Por exemplo, um pool de conexões com o banco de dados ou o carregamento de um modelo...