Default argument pos_label=1 is not ignored in f1_score metric for multiclass classification

### Describe the bug

I get a `ValueError` for `pos_label=1` default argument value to `f1_score` metric with argument `average='micro'` for the iris flower classification problem:

```pytb
ValueError: pos_label=1 is not a valid label: It should be one of ['setosa' 'versicolor' 'virginica']
```

According to the documentation, the `pos_label` argument should be ignored for the multiclass problem:

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#f1-score

_The class to report if `average='binary'` and the data is binary, otherwise this parameter is ignored._

Setting `pos_label` explicitly to None solves the problem and produces the expected output, see below.

### Steps/Code to Reproduce

```python
# Import necessary libraries
import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import make_scorer, f1_score

# Load the Iris dataset
data = load_iris()
X = data.data  # Features
y = data.target  # Labels

# Convert labels to string type
y = np.array([data.target_names[label] for label in data.target])

# Initialize the Linear Discriminant Analysis classifier
classifier = LinearDiscriminantAnalysis()

# Define a custom scorer using F1 score with average='micro'
f1_scorer = make_scorer(f1_score, average='micro', pos_label=1)

# Perform cross-validation with cross_val_score
try:
    scores = cross_val_score(classifier, X, y, cv=5, scoring=f1_scorer)
    print(f"Cross-validated F1 Scores (micro average): {scores}")
    print(f"Mean F1 Score: {np.mean(scores)}")
except ValueError as e:
    print(f"Error: {e}")
```

### Expected Results

```
Cross-validated F1 Scores (micro average): [1.         1.         0.96666667 0.93333333 1.        ]
Mean F1 Score: 0.9800000000000001
```

### Actual Results

```pytb
Cross-validated F1 Scores (micro average): [nan nan nan nan nan]
Mean F1 Score: nan
[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\model_selection\_validation.py:1000](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/model_selection/_validation.py#line=999): UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 139](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/metrics/_scorer.py#line=138), in __call__
    score = scorer._score(
            ^^^^^^^^^^^^^^
  File "[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 371](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/metrics/_scorer.py#line=370), in _score
    y_pred = method_caller(
             ^^^^^^^^^^^^^^
  File "[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\metrics\_scorer.py", line 89](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/metrics/_scorer.py#line=88), in _cached_call
    result, _ = _get_response_values(
                ^^^^^^^^^^^^^^^^^^^^^
  File "[C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\utils\_response.py", line 204](file:///C:/Users/rgt0227/AppData/Local/anaconda3/Lib/site-packages/sklearn/utils/_response.py#line=203), in _get_response_values
    raise ValueError(
ValueError: pos_label=1 is not a valid label: It should be one of ['setosa' 'versicolor' 'virginica']
```

### Versions

```shell
System:
    python: 3.11.5 | packaged by Anaconda, Inc. | (main, Sep 11 2023, 13:26:23) [MSC v.1916 64 bit (AMD64)]
executable: C:\Users\rgt0227\AppData\Local\anaconda3\python.exe
   machine: Windows-10-10.0.19045-SP0

Python dependencies:
      sklearn: 1.5.0
          pip: 23.2.1
   setuptools: 68.0.0
        numpy: 1.26.2
        scipy: 1.11.4
       Cython: None
       pandas: 2.1.1
   matplotlib: 3.8.0
       joblib: 1.2.0
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: mkl
    num_threads: 8
         prefix: mkl_rt
       filepath: C:\Users\rgt0227\AppData\Local\anaconda3\Library\bin\mkl_rt.2.dll
        version: 2023.1-Product
threading_layer: intel

       user_api: openmp
   internal_api: openmp
    num_threads: 8
         prefix: vcomp
       filepath: C:\Users\rgt0227\AppData\Local\anaconda3\Lib\site-packages\sklearn\.libs\vcomp140.dll
        version: None
```

COMMENT:
Removing good first issue label as  per:

> I don't really know what is the right fix here. We could have either to modify f1_score but it could be tricky. We could otherwise implement a workaround in the scorer but this is weird to special case this function. So we should carefully assess the problem and check what are all the possible resolution.

We should think about what the right fix is first

Labels: Bug