我有一个1GB的大目录，其中包含多个文件，这些文件以tar.gz格式存储在S3中，并由Lambda函数进行操作。
Lambda函数的文件系统是只读的。所以我希望操作在内存中完成。
我不能把它包含在Lambda函数本身的映像中，因为GitHub不接受这么大的文件。
让Lambda从S3读取它似乎是合理的，但是我不知道如何解压缩它。对不起，我是一个初学者。
下面是我写的：

# Define the resources to use
s3 = boto3.resource('s3', region_name='us-east-1')
bucket = s3.Bucket('tensorflow-models')
object = bucket.Object('saved-model.tar.gz')

# Prepare 2 file streams
file_stream1 = io.BytesIO()
file_stream2 = io.BytesIO()

# Download object to file stream
object.download_fileobj(file_stream1)

# Uncompress it
with tarfile.open(file_stream1, "r:gz") as tar:
    tar.extractall(file_stream2)

# Use it in Tensorflow
model = tf.keras.models.load_model(file_stream2)

# Get the result
result = model.call(embedded_sentences)

以下是错误消息：

{
  "errorMessage": "expected str, bytes or os.PathLike object, not BytesIO",
  "errorType": "TypeError",
  "requestId": "xxxxxxxxxxxxxxxxxxx",
  "stackTrace": [
    "  File \"/var/task/app.py\", line 87, in lambda_handler\n    with tarfile.open(file_stream1, \"r:gz\") as tar:\n",
    "  File \"/var/lang/lib/python3.9/tarfile.py\", line 1629, in open\n    return func(name, filemode, fileobj, **kwargs)\n",
    "  File \"/var/lang/lib/python3.9/tarfile.py\", line 1675, in gzopen\n    fileobj = GzipFile(name, mode + \"b\", compresslevel, fileobj)\n",
    "  File \"/var/lang/lib/python3.9/gzip.py\", line 173, in __init__\n    fileobj = self.myfileobj = builtins.open(filename, mode or 'rb')\n"
  ]
}