export.py replace `check_file` -> `check_yaml`
diff --git a/export.py b/export.py
index e3f6af93d1c..546087a4026 100644
--- a/export.py
+++ b/export.py
@@ -67,7 +67,7 @@
 from models.experimental import attempt_load
 from models.yolo import Detect
 from utils.dataloaders import LoadImages
-from utils.general import (LOGGER, check_dataset, check_file, check_img_size, check_requirements, check_version,
+from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, check_yaml,
                            colorstr, file_size, print_args, url2file)
 from utils.torch_utils import select_device
 
@@ -371,7 +371,7 @@ def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=c
         converter.optimizations = [tf.lite.Optimize.DEFAULT]
         if int8:
             from models.tf import representative_dataset_gen
-            dataset = LoadImages(check_dataset(check_file(data))['train'], img_size=imgsz, auto=False)
+            dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
             converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
             converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
             converter.target_spec.supported_types = []



## 🛠️ PR Summary

<sub>Made with ❤️ by [Ultralytics Actions](https://github.com/ultralytics/actions)<sub>

### 🌟 Summary
Enhancement of YAML dataset validation and reduction of dependencies in TensorFlow Lite export.

### 📊 Key Changes
- Removed `check_file` function and integrated `check_yaml` for dataset validation in TensorFlow Lite export process.
- Minor code cleanup in `export.py`, changing one line related to dataset loading.

### 🎯 Purpose & Impact
- 🎯 **Purpose**: The update ensures datasets are specifically validated as YAML files, improving reliability. Additionally, it contributes to cleaner and more maintainable code by using a more appropriate validation function.
- 🔍 **Impact**: Users exporting models to TensorFlow Lite may notice improved validation messages if there are issues with the dataset configuration. There should be no noticeable impact on the export functionality for users with correctly formatted datasets.