AttributeError "Flags object has no attribute 'c_contiguous'" when using KNeighborsClassifier predict

### Describe the bug

I tried to build a K-nearest neighbor model. I used the default ``weights`` parameter and called the ``predict`` method with a pandas DataFrame object and got the following error:

AttributeError: 'Flags' object has no attribute 'c_contiguous'

### Steps/Code to Reproduce

```python
import pandas as pd
import numpy as np
from sklearn.neighbors import KNeighborsClassifier

X = pd.DataFrame(np.array([[1, 2], [3, 4], [5, 6], [7, 8]]), columns=['a', 'b'])
y = np.array([1, 2, 3, 4])

knn1 = KNeighborsClassifier(n_neighbors=2)

knn1.fit(X, y)

knn1.predict(X)
```

### Expected Results

I expect to get a prediction from the knn1 model. Unfortunately, I get this error

### Actual Results
```
AttributeError                            Traceback (most recent call last)
Cell In[5], line 5
      1 knn1 = KNeighborsClassifier(n_neighbors=2)
      3 knn1.fit(X, y)
----> 5 knn1.predict(X)

File ~/.local/lib/python3.10/site-packages/sklearn/neighbors/_classification.py:246, in KNeighborsClassifier.predict(self, X)
    244 check_is_fitted(self, "_fit_method")
    245 if self.weights == "uniform":
--> 246     if self._fit_method == "brute" and ArgKminClassMode.is_usable_for(
    247         X, self._fit_X, self.metric
    248     ):
    249         probabilities = self.predict_proba(X)
    250         if self.outputs_2d_:

File ~/.local/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:471, in ArgKminClassMode.is_usable_for(cls, X, Y, metric)
    448 @classmethod
    449 def is_usable_for(cls, X, Y, metric) -> bool:
    450     """Return True if the dispatcher can be used for the given parameters.
    451 
    452     Parameters
   (...)
    468     True if the PairwiseDistancesReduction can be used, else False.
    469     """
    470     return (
--> 471         ArgKmin.is_usable_for(X, Y, metric)
    472         # TODO: Support CSR matrices.
    473         and not issparse(X)
    474         and not issparse(Y)
    475         # TODO: implement Euclidean specialization with GEMM.
    476         and metric not in ("euclidean", "sqeuclidean")
    477     )

File ~/.local/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:115, in BaseDistancesReductionDispatcher.is_usable_for(cls, X, Y, metric)
    101 def is_valid_sparse_matrix(X):
    102     return (
    103         isspmatrix_csr(X)
    104         and
   (...)
    110         X.indices.dtype == X.indptr.dtype == np.int32
    111     )
    113 is_usable = (
    114     get_config().get("enable_cython_pairwise_dist", True)
--> 115     and (is_numpy_c_ordered(X) or is_valid_sparse_matrix(X))
    116     and (is_numpy_c_ordered(Y) or is_valid_sparse_matrix(Y))
    117     and X.dtype == Y.dtype
    118     and X.dtype in (np.float32, np.float64)
    119     and metric in cls.valid_metrics()
    120 )
    122 return is_usable

File ~/.local/lib/python3.10/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:99, in BaseDistancesReductionDispatcher.is_usable_for.<locals>.is_numpy_c_ordered(X)
     98 def is_numpy_c_ordered(X):
---> 99     return hasattr(X, "flags") and X.flags.c_contiguous

AttributeError: 'Flags' object has no attribute 'c_contiguous'
```

### Versions

```shell
System:
    python: 3.10.6 (main, May 29 2023, 11:10:38) [GCC 11.3.0]
executable: /usr/bin/python3
   machine: Linux-5.15.0-76-generic-x86_64-with-glibc2.35

Python dependencies:
      sklearn: 1.3.0
          pip: 23.1.2
   setuptools: 68.0.0
        numpy: 1.25.0
        scipy: 1.11.1
       Cython: 0.29.35
       pandas: 2.0.3
   matplotlib: 3.7.1
       joblib: 1.3.1
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /home/werner/.local/lib/python3.10/site-packages/numpy.libs/libopenblas64_p-r0-7a851222.3.23.so
        version: 0.3.23
threading_layer: pthreads
   architecture: Haswell
    num_threads: 20

       user_api: openmp
   internal_api: openmp
         prefix: libgomp
       filepath: /home/werner/.local/lib/python3.10/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
    num_threads: 20

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: /home/werner/.local/lib/python3.10/site-packages/scipy.libs/libopenblasp-r0-23e5df77.3.21.dev.so
        version: 0.3.21.dev
threading_layer: pthreads
   architecture: Haswell
    num_threads: 20
```

COMMENT:
I am still facing the same error
`
#predicting the target value from the model for the samples
y_train_knn = knn.predict(X_train)
y_test_knn = knn.predict(X_test)
`

the error
`
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[33], line 2
      1 #predicting the target value from the model for the samples
----> 2 y_train_knn = knn.predict(X_train)
      3 y_test_knn = knn.predict(X_test)

File ~/anaconda3/lib/python3.11/site-packages/sklearn/neighbors/_classification.py:246, in KNeighborsClassifier.predict(self, X)
    244 check_is_fitted(self, "_fit_method")
    245 if self.weights == "uniform":
--> 246     if self._fit_method == "brute" and ArgKminClassMode.is_usable_for(
    247         X, self._fit_X, self.metric
    248     ):
    249         probabilities = self.predict_proba(X)
    250         if self.outputs_2d_:

File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:471, in ArgKminClassMode.is_usable_for(cls, X, Y, metric)
    448 @classmethod
    449 def is_usable_for(cls, X, Y, metric) -> bool:
    450     """Return True if the dispatcher can be used for the given parameters.
    451 
    452     Parameters
   (...)
    468     True if the PairwiseDistancesReduction can be used, else False.
    469     """
    470     return (
--> 471         ArgKmin.is_usable_for(X, Y, metric)
    472         # TODO: Support CSR matrices.
    473         and not issparse(X)
    474         and not issparse(Y)
    475         # TODO: implement Euclidean specialization with GEMM.
    476         and metric not in ("euclidean", "sqeuclidean")
    477     )

File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:115, in BaseDistancesReductionDispatcher.is_usable_for(cls, X, Y, metric)
    101 def is_valid_sparse_matrix(X):
    102     return (
    103         isspmatrix_csr(X)
    104         and
   (...)
    110         X.indices.dtype == X.indptr.dtype == np.int32
    111     )
    113 is_usable = (
    114     get_config().get("enable_cython_pairwise_dist", True)
--> 115     and (is_numpy_c_ordered(X) or is_valid_sparse_matrix(X))
    116     and (is_numpy_c_ordered(Y) or is_valid_sparse_matrix(Y))
    117     and X.dtype == Y.dtype
    118     and X.dtype in (np.float32, np.float64)
    119     and metric in cls.valid_metrics()
    120 )
    122 return is_usable

File ~/anaconda3/lib/python3.11/site-packages/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:99, in BaseDistancesReductionDispatcher.is_usable_for.<locals>.is_numpy_c_ordered(X)
     98 def is_numpy_c_ordered(X):
---> 99     return hasattr(X, "flags") and X.flags.c_contiguous

AttributeError: 'Flags' object has no attribute 'c_contiguous'
`

Name: numpy
Version: 1.26.3
Name: scikit-learn
Version: 1.2.2

Labels: Bug