Fix ResourceExhaustedError

✅ Solution

ResourceExhaustedError usually arises when TensorFlow tries to allocate memory beyond available resources (GPU or RAM). Reduce batch sizes, model complexity, or input data dimensions to decrease memory footprint, or move computation to a device with more memory if possible. Consider enabling memory growth using `tf.config.experimental.set_memory_growth` or using mixed precision training (`tf.keras.mixed_precision.Policy`) to further mitigate memory usage.