parameters: (cls, selectable, sampling, name=None, seed=None)
return_statement: return coercions.expect(roles.FromClauseRole, selectable).tablesample(
              sampling, name=name, seed=seed
          )
docstring: Return a :class:`.TableSample` object.
  
          :class:`.TableSample` is an :class:`.Alias` subclass that represents
          a table with the TABLESAMPLE clause applied to it.
          :func:`~.expression.tablesample`
          is also available from the :class:`.FromClause` class via the
          :meth:`.FromClause.tablesample` method.
  
          The TABLESAMPLE clause allows selecting a randomly selected approximate
          percentage of rows from a table. It supports multiple sampling methods,
          most commonly BERNOULLI and SYSTEM.
  
          e.g.::
  
              from sqlalchemy import func
  
              selectable = people.tablesample(
                          func.bernoulli(1),
                          name='alias',
                          seed=func.random())
              stmt = select([selectable.c.people_id])
  
          Assuming ``people`` with a column ``people_id``, the above
          statement would render as::
  
              SELECT alias.people_id FROM
              people AS alias TABLESAMPLE bernoulli(:bernoulli_1)
              REPEATABLE (random())
  
          .. versionadded:: 1.1
  
          :param sampling: a ``float`` percentage between 0 and 100 or
              :class:`.functions.Function`.
  
          :param name: optional alias name
  
          :param seed: any real-valued SQL expression.  When specified, the
           REPEATABLE sub-clause is also rendered.
docstring_summary: Return a :class:`.TableSample` object.
docstring_tokens: Return a : class : . TableSample object .
function: def _factory(cls, selectable, sampling, name=None, seed=None):
          """Return a :class:`.TableSample` object.
  
          :class:`.TableSample` is an :class:`.Alias` subclass that represents
          a table with the TABLESAMPLE clause applied to it.
          :func:`~.expression.tablesample`
          is also available from the :class:`.FromClause` class via the
          :meth:`.FromClause.tablesample` method.
  
          The TABLESAMPLE clause allows selecting a randomly selected approximate
          percentage of rows from a table. It supports multiple sampling methods,
          most commonly BERNOULLI and SYSTEM.
  
          e.g.::
  
              from sqlalchemy import func
  
              selectable = people.tablesample(
                          func.bernoulli(1),
                          name='alias',
                          seed=func.random())
              stmt = select([selectable.c.people_id])
  
          Assuming ``people`` with a column ``people_id``, the above
          statement would render as::
  
              SELECT alias.people_id FROM
              people AS alias TABLESAMPLE bernoulli(:bernoulli_1)
              REPEATABLE (random())
  
          .. versionadded:: 1.1
  
          :param sampling: a ``float`` percentage between 0 and 100 or
              :class:`.functions.Function`.
  
          :param name: optional alias name
  
          :param seed: any real-valued SQL expression.  When specified, the
           REPEATABLE sub-clause is also rendered.
  
          """
          return coercions.expect(roles.FromClauseRole, selectable).tablesample(
              sampling, name=name, seed=seed
          )
function_tokens: def _factory ( cls , selectable , sampling , name = None , seed = None ) : return coercions . expect ( roles . FromClauseRole , selectable ) . tablesample ( sampling , name = name , seed = seed )