check_classifiers_train fails when tag requires_positive_X=True

### Describe the bug

The check `check_classifiers_train` fails for classifiers that need positive X, even when the tag `requires_positive_X` is set to `True`.

In the example below, I copy/paste the template from [skltemplate](https://github.com/scikit-learn-contrib/project-template/blob/master/skltemplate/_template.py) and add a check in `fit` to mimic the behaviour of classifiers that need positive X. The tag `requires_positive_X` is set to `True` in `_more_tags()`



### Steps/Code to Reproduce

```python
"""
This is a module to be used as a reference for building other modules
"""
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import euclidean_distances
from sklearn.utils.estimator_checks import parametrize_with_checks


class TemplateClassifier(ClassifierMixin, BaseEstimator):
    """ An example classifier which implements a 1-NN algorithm.
    For more information regarding how to build your own classifier, read more
    in the :ref:`User Guide <user_guide>`.
    Parameters
    ----------
    demo_param : str, default='demo'
        A parameter used for demonstation of how to pass and store paramters.
    Attributes
    ----------
    X_ : ndarray, shape (n_samples, n_features)
        The input passed during :meth:`fit`.
    y_ : ndarray, shape (n_samples,)
        The labels passed during :meth:`fit`.
    classes_ : ndarray, shape (n_classes,)
        The classes seen at :meth:`fit`.
    """
    def __init__(self, demo_param='demo'):
        self.demo_param = demo_param

    def fit(self, X, y):
        """A reference implementation of a fitting function for a classifier.
        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            The training input samples.
        y : array-like, shape (n_samples,)
            The target values. An array of int.
        Returns
        -------
        self : object
            Returns self.
        """
        # Check that X and y have correct shape
        X, y = check_X_y(X, y)

        # Check non-negative X for illustration
        if np.any(X < 0):
            raise ValueError("This classifier needs non-negative X")

        # Store the classes seen during fit
        self.classes_ = unique_labels(y)
        self.n_features_in_ = X.shape[1]
        self.X_ = X
        self.y_ = y
        # Return the classifier
        return self

    def predict(self, X):
        """ A reference implementation of a prediction for a classifier.
        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            The input samples.
        Returns
        -------
        y : ndarray, shape (n_samples,)
            The label for each sample is the label of the closest sample
            seen during fit.
        """
        # Check is fit had been called
        check_is_fitted(self, ['X_', 'y_'])

        # Input validation
        X = check_array(X)

        closest = np.argmin(euclidean_distances(X, self.X_), axis=1)
        return self.y_[closest]

    def _more_tags(self):
        return {
            'requires_y': True,
            'requires_positive_X': True,
        }


@parametrize_with_checks([TemplateClassifier()])
def test_sklearn_compatible_estimator(estimator, check):
    check(estimator)

```

### Expected Results

I would expect that checks involving negative X are skipped when tag `requires_positive_X` is set to `True`

### Actual Results

```bash
=========================================================================== short test summary info ===========================================================================
FAILED bornclassifier.py::test_sklearn_compatible_estimator[TemplateClassifier()-check_classifiers_train] - ValueError: This classifier needs non-negative X
FAILED bornclassifier.py::test_sklearn_compatible_estimator[TemplateClassifier()-check_classifiers_train(readonly_memmap=True)] - ValueError: This classifier needs non-nega...
FAILED bornclassifier.py::test_sklearn_compatible_estimator[TemplateClassifier()-check_classifiers_train(readonly_memmap=True,X_dtype=float32)] - ValueError: This classifie...
================================================================== 3 failed, 40 passed, 4 warnings in 0.99s ===================================================================
```

### Versions

```shell
System:
    python: 3.9.10 (main, Mar 16 2022, 16:37:35)  [Clang 13.0.0 (clang-1300.0.27.3)]
   machine: macOS-12.2.1-arm64-i386-64bit
Python dependencies:
      sklearn: 1.1.2
          pip: 22.0.3
   setuptools: 60.6.0
        numpy: 1.22.3
        scipy: 1.8.0
       Cython: None
       pandas: 1.4.1
   matplotlib: 3.5.1
       joblib: 1.1.0
threadpoolctl: 3.1.0
Built with OpenMP: True
threadpoolctl info:
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
        version: 0.3.18
threading_layer: pthreads
   architecture: armv8
    num_threads: 8
       user_api: openmp
   internal_api: openmp
         prefix: libomp
        version: None
    num_threads: 8
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
        version: 0.3.17
threading_layer: pthreads
   architecture: armv8
    num_threads: 8
```

COMMENT:
Solved in https://github.com/scikit-learn/scikit-learn/pull/24667

Labels: Bug, module:utils, module:test-suite