timestamp mismatch when using code_location

HI,

When code_location is used in estimator of TrainingStep(), the uploaded s3 path and sagemaker_submit_directory timestamp do not match(about 400 ms).
This will cause the execution to fail.

In SageMaker training job, timestamp matches even if code_location is used.

S3 uploaded path
s3://my-bucket/model/sagemaker-xgboost-2020-06-10-06-29-37-910/source/sourcedir.tar.gz

sagemaker_submit_directory
"s3://my-bucket/model/sagemaker-xgboost-2020-06-10-06-29-38-323/source/sourcedir.tar.gz"

```
# Open Source distributed script mode
from sagemaker.session import s3_input, Session
from sagemaker.xgboost.estimator import XGBoost

boto_session = boto3.Session(region_name=region)
session = Session(boto_session=boto_session)

output_path = 's3://{}/{}'.format(bucket_name, 'model')

xgb_script_mode_estimator = XGBoost(
entry_point='xgboost.py',
source_dir='source',
framework_version='0.90-2', # Note: framework_version is mandatory
hyperparameters=hyperparams,
role=role,
train_instance_count=1,
train_instance_type='ml.m5.2xlarge',
code_location=output_path, # ← Cause a mismatch
output_path=output_path
)
```

Start with the TrainingStep estimator and the code_location handling shown in the reproduction. Run the provided XGBoost example and compare the S3 uploaded path with sagemaker_submit_directory timestamps. Done means both paths use the same timestamp so the training execution succeeds.

Written by the indexing model from the issue text.