refactor code to calculate records per shard using n_volumes and number of shards

[hvgazula]

https://github.com/neuronets/nobrainer/blob/976691d685824fd4bba836498abea4184cffd798/nobrainer/dataset.py#L115-L122

If the number of volumes in the shard is too large, this snippet of code can be time-consuming. Alternatives are

• use a combination of n_volumes and number of files with file_pattern to calculate len(first_shard)
• provide metadata (number of volumes in the shard) as well as total number of volumes in the dataset

[hvgazula]

code for option 1: https://github.com/neuronets/nobrainer_training_scripts/blob/784c8668ae01356173faffbcf860bca458f46a73/1.2.0/create_tfshards.py#L307-L311 should now work after https://github.com/neuronets/nobrainer/issues/329

[hvgazula]

Ideally, if the tfrecords are created using the API, with the aforementioned change, we can ensure the same number of records in every shard except the last one. Now, if n_volumes is not specified, it can be calculated using this function, which is num_records_first_shard * (num_shards - 1) + num_records_in_last_shard