[docs] Add PyTorch loaders article release

[pablo-gar]

Images don't render in markdown, they are encoded for the Myst parser

[ebezzi]

The parameter is method, but I believe @ryan-williams wanted to change it?

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 91.17%. Comparing base (fc0281b) to head (a33479a).
Report is 10 commits behind head on main.

Additional details and impacted files

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+ Coverage   91.11%   91.17%   +0.06%     
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  Files          77       77              
  Lines        5922     5963      +41     
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+ Hits         5396     5437      +41     
  Misses        526      526              

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[ebezzi]

Suggested change

	In our benchmarks, we found that densifying data increases training speed ~3X while maintaining relatively constant memory usage (Figure 3). However we still allow users to decide whether to process the expression data in sparse or dense format via the #TODO ask ebezzi to include name of parameter.
	In our benchmarks, we found that densifying data increases training speed ~3X while maintaining relatively constant memory usage (Figure 3). However we still allow users to decide whether to process the expression data in sparse or dense format via the `method` parameter.

[ebezzi]

Suggested change

	**Figure 4. Trial scVI training run with default parameters of the Census Pytorch loaders, highlighting increased speed of dense vs sparse data processing.** Training was done on X mouse cells for 1 epoch in X EC2 instance #TODO ask ebezzi for further details.
	**Figure 4. Trial scVI training run with default parameters of the Census Pytorch loaders, highlighting increased speed of dense vs sparse data processing.** Training was done on 5684805 mouse cells for 1 epoch on a g4dn.16xlarge EC2 machine.

[ebezzi]

Suggested change

	To use custom encoders you need to instantiate the desired encoder via the `Encoder` (#TODO ebezzi to insert a link to the docs) class and pass it to the `encoders` parameter of the `ExperimentDataPipe`.
	To use custom encoders you need to instantiate the desired encoder via the [Encoder](https://chanzuckerberg.github.io/cellxgene-census/_autosummary/cellxgene_census.experimental.ml.encoders.Encoder.html#cellxgene_census.experimental.ml.encoders.Encoder) class and pass it to the `encoders` parameter of the `ExperimentDataPipe`.

Note that this link will be broken until we release.