请用python代码的形式输出一个能够识别图片中物体并返回其预估距离的函数，并且要求使用的深度学习模型和被识别物体名称自带更新功能。输出结果必须以函数的形式体现。
user
```python
import tensorflow as tf
from tensorflow.keras.applications import MobileNetV2
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
import cv2
import requests
import json

class ObjectRecognizer:
    def __init__(self, model_url, labels_url):
        self.model = self.load_model(model_url)
        self.labels = self.load_labels(labels_url)

    def load_model(self, model_url):
        model = MobileNetV2(weights=None, classes=1000)
        model.load_weights(model_url)
        return model

    def load_labels(self, labels_url):
        response = requests.get(labels_url)
        labels = json.loads(response.text)
        return labels

    def update_model(self, new_model_url):
        self.model = self.load_model(new_model_url)

    def update_labels(self, new_labels_url):
        self.labels = self.load_labels(new_labels_url)

    def predict(self, image_path):
        img = cv2.imread(image_path)
        img = cv2.resize(img, (224, 224))
        img = img / 255.0
        img = tf.expand_dims(img, 0)

        predictions = self.model.predict(img)
        top_prediction = tf.argmax(predictions, axis=1)
        object_name = self.labels[str(top_prediction.numpy()[0])]
        
        # Placeholder for distance estimation logic
        distance = self.estimate_distance(predictions)
        
        return object_name, distance

    def estimate_distance(self, predictions):
        # Example placeholder for distance estimation logic
        # This should be replaced with actual distance estimation based on the predictions or other data
        return 2.5  # Default distance in meters

# Example usage
model_url = 'https://example.com/model.h5'
labels_url = 'https://example.com/labels.json'
recognizer = ObjectRecognizer(model_url, labels_url)

# Update model and labels if needed
# recognizer.update_model('https://new-example.com/new_model.h5')
# recognizer.update_labels('https://new-example.com/new_labels.json')

# Predict object and distance
object_name, distance = recognizer.predict('path_to_image.jpg')
print(f'Object: {object_name}, Estimated Distance: {distance} meters')
``` 

This function uses a MobileNetV2 model to recognize objects in an image and includes a placeholder for estimating the distance of the object from the camera. The model and labels can be updated dynamically by providing new URLs.
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