add tutorial notebooks (PR 7 of N)

[bkmartinjr]

Add three notebooks (some are partial - functional but need additional verbiage before release).

• tutorial_pytorch: demonstrate toy model using a DataPipe
• tutorial_lightning: demonstrate toy model using Lighnting
• tutorial_multiworker: demo sharp edges with distributed/multi-worker usage (e.g., set_epoch)

[aaronwolen]

The notebooks are really helpful and ran perfectly for me. I made a few minor comments/suggestions.

[aaronwolen]

Suggested change

"This class provides an implementation of PyTorch's `torchdata` [IterDataPipe interface](https://pytorch.org/data/main/torchdata.datapipes.iter.html), which defines a common mechanism for wrapping and accessing training data from any underlying source. The `ExperimentAxisQueryIterDataPipe` class encapsulates the details of querying and retrieving Census data from a single SOMA `Experiment` and returning it to the caller a NumPy `ndarray` and a Pandas `DataFrame`. Most importantly, it retrieves the data lazily from the Census in batches, avoiding having to load the entire training dataset into memory at once.\n",

"This class provides an implementation of PyTorch's `torchdata` [IterDataPipe interface](https://pytorch.org/data/main/torchdata.datapipes.iter.html), which defines a common mechanism for wrapping and accessing training data from any underlying source. The `ExperimentAxisQueryIterDataPipe` class encapsulates the details of querying and retrieving data from a single SOMA `Experiment` and returning to the caller a NumPy `ndarray` and a Pandas `DataFrame`. Most importantly, it retrieves the data lazily and in batches, avoiding the need to load the entire training dataset into memory at once.\n",

I removed a couple references to the Census because the wording made it sound like this is specific to the Census.

[aaronwolen]

Suggested change

	"The values for the prediction label(s) that you intend to use for training are specified via the `obs_column_names` array.\n",
	"The values for the prediction label(s) that you intend to use for training are specified via the `obs_column_names` (or `var_column_names`) array.\n",

[aaronwolen]

Suggested change

	"The `batch_size` allows you to specify the number of obs rows (cells) to be returned by each return PyTorch tensor. You may exclude this parameter if you want single rows (`batch_size=1`).\n",
	"The `batch_size` parameter allows you to specify the number of `obs` rows (i.e., cells) to be returned by each return PyTorch tensor. You may exclude this parameter if you want single rows (`batch_size=1`).\n",

[aaronwolen]

Suggested change

"* PyTorch's TorchData library provides a [Shuffler](https://pytorch.org/data/main/generated/torchdata.datapipes.iter.Shuffler.html) `DataPipe`, which is alternate mechanism one can use to perform shuffling of an `IterableDataset`. However, the `Shuffler` will not \"globally\" randomize the training data, as it only \"locally\" randomizes the ordering of the training data within fixed-size \"windows\". Due to the layout of Census data, a given \"window\" of Census data may be highly homogeneous in terms of its `obs` axis attribute values, and so this shuffling strategy may not provide sufficient randomization for certain types of models."

"* PyTorch's TorchData library provides a [Shuffler](https://pytorch.org/data/main/generated/torchdata.datapipes.iter.Shuffler.html) `DataPipe`, which is an alternate mechanism one can use to perform shuffling of an `IterableDataset`. However, the `Shuffler` will not \"globally\" randomize the training data, as it only \"locally\" randomizes the ordering of the training data within fixed-size \"windows\". This problematic for atlas-style datasets such as Census, where a given \"window\" of data may be highly homogeneous in terms of its `obs` axis attribute values, and so this shuffling strategy may not provide sufficient randomization for certain types of models."

[aaronwolen]

Does pytorch_lightning need to be listed here too?

[aaronwolen]

Suggested change

	"* using the `torch.utils.data.DataLoader` with 1 or more worker (ie., with an argument of `n_workers=1` or greater)\n",
	"* using the `torch.utils.data.DataLoader` with 1 or more workers (i.e., with an argument of `n_workers=1` or greater)\n",