AttributeError: This 'LabelEncoder' has no attribute 'set_output'

### Describe the bug

I tried to call **'set_output'** from LabelEncoder object and got the AttributeError.

[The document](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html) says sklearn.preprocessing.LabelEncoder has **'set_output'** method, but it was not working.

Soon I found most of other **'set_output'** available estimators inherits both of sklearn.base.OneToOneFeatureMixin and sklearn.base.TransformerMinxin

Howerver, [LabelEncoder](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/preprocessing/_label.py) only inherits the TransformerMinxin.

```python
class LabelEncoder(TransformerMixin, BaseEstimator):
```

</br>

Function **'set_output'** seems available when **'_auto_wrap_is_configured'** is True. [(utils._set_output.py)](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/_set_output.py)

```python
    @available_if(_auto_wrap_is_configured)
    def set_output(self, *, transform=None):
        """Set output container.

        See :ref:`sphx_glr_auto_examples_miscellaneous_plot_set_output.py`
        for an example on how to use the API.

        Parameters
        ----------
        transform : {"default", "pandas"}, default=None
            Configure output of `transform` and `fit_transform`.

            - `"default"`: Default output format of a transformer
            - `"pandas"`: DataFrame output
            - `None`: Transform configuration is unchanged

        Returns
        -------
        self : estimator instance
            Estimator instance.
        """
        if transform is None:
            return self

        if not hasattr(self, "_sklearn_output_config"):
            self._sklearn_output_config = {}

        self._sklearn_output_config["transform"] = transform
        return self
```

</br>

Then estimator should have **'get_feature_names_out'** to make **'_auto_wrap_is_configured'** returns True. [(utils._set_output.py)](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/utils/_set_output.py)

```python
def _auto_wrap_is_configured(estimator):
    """Return True if estimator is configured for auto-wrapping the transform method.

    `_SetOutputMixin` sets `_sklearn_auto_wrap_output_keys` to `set()` if auto wrapping
    is manually disabled.
    """
    auto_wrap_output_keys = getattr(estimator, "_sklearn_auto_wrap_output_keys", set())
    return (
        hasattr(estimator, "get_feature_names_out")
        and "transform" in auto_wrap_output_keys
    )
```

</br>

 To have **'get_feature_names_out'** attr, estimator should inherit OneToOneFeatureMixin as I think. [(base.py)](https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/base.py)

```python
class OneToOneFeatureMixin:
    """Provides `get_feature_names_out` for simple transformers.

    This mixin assumes there's a 1-to-1 correspondence between input features
    and output features, such as :class:`~preprocessing.StandardScaler`.
    """

    def get_feature_names_out(self, input_features=None):
        """Get output feature names for transformation.

        Parameters
        ----------
        input_features : array-like of str or None, default=None
            Input features.

            - If `input_features` is `None`, then `feature_names_in_` is
              used as feature names in. If `feature_names_in_` is not defined,
              then the following input feature names are generated:
              `["x0", "x1", ..., "x(n_features_in_ - 1)"]`.
            - If `input_features` is an array-like, then `input_features` must
              match `feature_names_in_` if `feature_names_in_` is defined.

        Returns
        -------
        feature_names_out : ndarray of str objects
            Same as input features.
        """
        check_is_fitted(self, "n_features_in_")
        return _check_feature_names_in(self, input_features)
```

</br>

I want to know that it is kind of a bug, or the document says wrong information.

### Steps/Code to Reproduce

```python
from sklearn.preprocessing import LabelEncoder

LabelEncoder().set_output()
```

### Expected Results

No error is thrown

### Actual Results

```python
--------------------------------------------------------------------------------
AttributeError                                 Traceback (most recent call last)                        
/tmp/ipykernel_16765/3091610596.py in <module>
---> 1 LabelEncoder().set_output()

~/.local/lib/python3.8/site-packages/sklearn/utils/_available_if.py in __get__(self, obj, owner)
      31        # this is to allow access to the docstrings.
      32        if not self.check(obj):
--->  33            raise attr_err
      34        out = MethodType(self.fn, obj)
      35
AttributeError: This 'LabelEncoder' has no attribute 'set_output'
```
            
             
            

### Versions

```shell
1.2.2
```

COMMENT:
Apparently `LabelEncoder` is not meant to be used on 2D inputs:

```python
In [2]: import numpy as np

In [3]: labels = np.asarray(["a", "a", "b"])

In [4]: from sklearn.preprocessing import LabelEncoder

In [5]: LabelEncoder().fit_transform(labels)
Out[5]: array([0, 0, 1])

In [6]: LabelEncoder().fit_transform(labels.reshape(-1, 1))
/Users/ogrisel/code/scikit-learn/sklearn/preprocessing/_label.py:114: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
  y = column_or_1d(y, warn=True)
Out[6]: array([0, 0, 1])
```

so I would never expect to output a 2D datastructure like `pandas.DataFrame` but we could output a `pandas.Series` if the input is also a `Series` and preserve its name.

But this is quite different from the usual `get_feature_names_out` logic.

Labels: Bug, Documentation, good first issue, help wanted