code: def integer() -> SelectorType:
      """
      Select all integer columns.
  
      See Also
      --------
      by_dtype : Select columns by dtype.
      float : Select all float columns.
      numeric : Select all numeric columns.
      signed_integer : Select all signed integer columns.
      unsigned_integer : Select all unsigned integer columns.
  
      Examples
      --------
      >>> import polars.selectors as cs
      >>> df = pl.DataFrame(
      ...     {
      ...         "foo": ["x", "y"],
      ...         "bar": [123, 456],
      ...         "baz": [2.0, 5.5],
      ...         "zap": [0, 1],
      ...     }
      ... )
  
      Select all integer columns:
  
      >>> df.select(cs.integer())
      shape: (2, 2)
      ┌─────┬─────┐
      │ bar ┆ zap │
      │ --- ┆ --- │
      │ i64 ┆ i64 │
      ╞═════╪═════╡
      │ 123 ┆ 0   │
      │ 456 ┆ 1   │
      └─────┴─────┘
  
      Select all columns *except* for those that are integer :
  
      >>> df.select(~cs.integer())
      shape: (2, 2)
      ┌─────┬─────┐
      │ foo ┆ baz │
      │ --- ┆ --- │
      │ str ┆ f64 │
      ╞═════╪═════╡
      │ x   ┆ 2.0 │
      │ y   ┆ 5.5 │
      └─────┴─────┘
  
      """
      return _selector_proxy_(F.col(INTEGER_DTYPES), name="integer")
  
imports: from __future__ import annotations
  import re
  from datetime import timezone
  from functools import reduce
  from operator import or_
  from typing import TYPE_CHECKING, Any, Collection, Literal, Mapping, overload
  from polars import functions
  from polars.datatypes import FLOAT_DTYPES, INTEGER_DTYPES, NUMERIC_DTYPES, SIGNED_INTEGER_DTYPES, TEMPORAL_DTYPES, UNSIGNED_INTEGER_DTYPES, Binary, Boolean, Categorical, Date, Datetime, Decimal, Duration, Object, String, Time, is_polars_dtype
  from polars.expr import Expr
  from polars.utils.deprecation import deprecate_nonkeyword_arguments
  from polars.utils.various import is_column
label_desc: Use image node_id 24 for calling a global function with example usage: integer() and returns: _selector_proxy_