Numpy Array Error when Training LogisticRegressionCV

### Describe the bug

When I attempt to train LogisticRegressionCV, I get the error: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.

The inputs to LogisticRegressionCV are:
- X = output of StandardScaler.fit_transform, with a shape of (24, 12) and dtype of float64
- y = array with shape (24, ) and dtype of int64

I checked that there are not null or infinite values in either array.

### Steps/Code to Reproduce

```
data_for_color_training = training_data.loc[training_data["IdentCode"] == color]
x_train = data_for_color_training.loc[:, (data_for_color_training.columns.str.startswith("dx") | data_for_color_training.columns.str.startswith("dy") | data_for_color_training.columns.str.startswith("dz"))]
y_train = data_for_color_training["Grade"]
			
#--------------Edit specific pre-processing and model here-----------------------------
scaler = StandardScaler()
X_train = scaler.fit_transform(x_train)
modelTrained = LogisticRegressionCV(max_iter=10000).fit(X_train, y_train)
```

### Expected Results

The variable modelTrained will output a LogisticRegressionCV model.

### Actual Results
```traceback
Traceback (most recent call last):
  File "C:\Users\...\PythonScripts\UserDefinedModel.py", line 22, in train_predict_by_colorcode
    modelTrained = LogisticRegressionCV(max_iter=10000).fit(X_train, y_train)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\linear_model\_logistic.py", line 1912, in fit
    coefs_paths = np.reshape(
                  ^^^^^^^^^^^
  File "<__array_function__ internals>", line 200, in reshape
  File "C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\core\fromnumeric.py", line 298, in reshape
    return _wrapfunc(a, 'reshape', newshape, order=order)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\core\fromnumeric.py", line 54, in _wrapfunc
    return _wrapit(obj, method, *args, **kwds)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\core\fromnumeric.py", line 43, in _wrapit
    result = getattr(asarray(obj), method)(*args, **kwds)
                     ^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\core\fromnumeric.py", line 298, in reshape
    return _wrapfunc(a, 'reshape', newshape, order=order)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\core\fromnumeric.py", line 54, in _wrapfunc
    return _wrapit(obj, method, *args, **kwds)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\core\fromnumeric.py", line 43, in _wrapit
    result = getattr(asarray(obj), method)(*args, **kwds)
                     ^^^^^^^^^^^^
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5, 10) + inhomogeneous part.
```
### Versions

```shell
System:
    python: 3.11.1 (tags/v3.11.1:a7a450f, Dec  6 2022, 19:58:39) [MSC v.1934 64 bit (AMD64)]
executable: C:\Users\...\AppData\Local\Programs\Python\Python311\python.exe
   machine: Windows-10-10.0.19044-SP0

Python dependencies:
      sklearn: 1.2.2
          pip: 22.3.1
   setuptools: 65.5.0
        numpy: 1.24.1
        scipy: 1.10.0
       Cython: None
       pandas: 1.5.3
   matplotlib: 3.6.3
       joblib: 1.2.0
threadpoolctl: 3.1.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\numpy\.libs\libopenblas64__v0.3.21-gcc_10_3_0.dll
        version: 0.3.21
threading_layer: pthreads
   architecture: Haswell
    num_threads: 16

       user_api: openmp
internal_api: openmp
         prefix: vcomp
       filepath: C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\.libs\vcomp140.dll
        version: None
    num_threads: 16

       user_api: blas
   internal_api: openblas
         prefix: libopenblas
       filepath: C:\Users\...\AppData\Local\Programs\Python\Python311\Lib\site-packages\scipy.libs\libopenblas-802f9ed1179cb9c9b03d67ff79f48187.dll
        version: 0.3.18
threading_layer: pthreads
   architecture: Haswell
    num_threads: 16
```

COMMENT:
> Not sure how I could provide a data example here. @adrinjalali

Try to find out what is unique about your data that causes this. Downsize your data and simplify it to the utmost until the problem no longer occurs. The simplest instance in which it occurs should _hopefully_ make the triggering aspects a bit more obvious. Try to then create an easy, small synthetic data example with the same inherent properties which still triggers the error.

It is very hard for us to work backwards from provided data, and sometimes indeed _unsafe_ for us to do so, thus small reproducers from those reporting the error are the best way to move the issue forwards

Labels: Bug, module:linear_model