Fix Cannot convert [array()] to EagerTensor of dtype int64 (#31109)

While running the model.prepare_tf_dataset() method,
it raises the error below:
```
TypeError: Cannot convert [array([322.,   1.])] to EagerTensor of dtype int64
```

This happens, in  "DataCollatorForSeq2Seq" function when we are try
to convert the labels to tensors. While converting the labels to tensors,
the labels can be in the format of list of list or list of ndarrays.
There is no problem converting the list of list lables. There is a problem
when the list of ndarrays are float values(like below).

```
[array([322.,   1.])]
```

so the exception raises while trying to convert this label to tensors using
below code.

```
batch["labels"] = tf.constant(batch["labels"], dtype=tf.int64)
```

The labels are always integer values, so this got converted to float
values in the label padding operation below.
```
batch["labels"] = [
                    call(label)
                    if padding_side == "right"
    ...

src/transformers/data/data_collator.py

@@ -632,9 +632,19 @@ class DataCollatorForSeq2Seq:
                     ]
                 else:
                     batch["labels"] = [
-                        np.concatenate([label, [self.label_pad_token_id] * (max_label_length - len(label))])
+                        np.concatenate(
+                            [
+                                label,
+                                np.array([self.label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),
+                            ]
+                        )
                         if padding_side == "right"
-                        else np.concatenate([[self.label_pad_token_id] * (max_label_length - len(label)), label])
+                        else np.concatenate(
+                            [
+                                np.array([self.label_pad_token_id] * (max_label_length - len(label)), dtype=np.int64),
+                                label,
+                            ]
+                        )
                         for label in labels
                     ]