fed_cifar10 sample does not download the dataset correctly

I'm running this [example](python/examples/federate/cross_silo/mqtt_s3_fedavg_cifar10_lr_example).I use one Jetson device as the server side and the other as the client side.This is my config.yaml source file.
```
common_args:
training_type: "cross_silo"
scenario: "horizontal"
using_mlops: false
random_seed: 0
config_version: release

environment_args:
bootstrap: config/bootstrap.sh

data_args:
dataset: "cifar10"
data_cache_dir: "~/fedcv_data/"
partition_method: "hetero"
partition_alpha: 0.5

model_args:
model: "mobilenet_v3"
image_size:
input_size: 3
class_num: 10
model_file_cache_folder: "./model_file_cache" # will be filled by the server automatically
global_model_file_path: "./model_file_cache/global_model.pt"

train_args:
federated_optimizer: "FedAvg"
client_id_list:
client_num_in_total: 2
client_num_per_round: 2
comm_round: 10
epochs: 1
batch_size: 4
client_optimizer: sgd
lr: 0.01
weight_decay: 0.001

validation_args:
frequency_of_the_test: 1

device_args:
worker_num: 2
using_gpu: true
# gpu_mapping_file: config/gpu_mapping.yaml
# gpu_mapping_key: mapping_default

comm_args:
backend: "MQTT_S3"
mqtt_config_path: config/mqtt_config.yaml
s3_config_path: config/s3_config.yaml

tracking_args:
# When running on MLOps platform(open.fedml.ai), the default log path is at ~/fedml-client/fedml/logs/ and ~/fedml-server/fedml/logs/
enable_wandb: false
wandb_key: ee0b5f53d949c84cee7decbe7a629e63fb2f8408
wandb_project: fedml
wandb_name: fedml_torch_image_classification
```

But when I change the [download=False](https://github.com/FedML-AI/FedML/blob/3e6a399e86ca808d065dc918b532e7173b918e91/python/fedml/data/cifar10/without_reload.py#L19) and [self.download = download](https://github.com/FedML-AI/FedML/blob/3e6a399e86ca808d065dc918b532e7173b918e91/python/fedml/data/cifar10/without_reload.py#L26C9-L26C33) in this file to `download=True`, the dataset is downloaded and run.

- My environment is as follows

- My device is agx jetson xavier.The docker container version used is nvidia-jetson-l4t-ml-r35.1.0-py3.