Explain: Add PGExplainer

[wsad1]

This PR makes the following changes.

1. Adds PGExplainer a new explainer algorithm. Paper , Reference Implementation
2. Updated the Explainer and ExplainerAlgorithm interface to support PGExplainer. Since PGExplainer uses a mlp to explain predictions that mlp needs to be trained before we call Explainer.forward.
   The usage of PGExplainer would look like:

  explainer = Explainer(
      model=model,
      algorithm=PGExplainer(epochs=2),
      explanation_type=explanation_type,
      node_mask_type=None, # pg explainer only generates edge masks
      edge_mask_type=edge_mask_type,
      model_config=model_config,)
explainer.train_explainer_algorithm(x, edge_index , target)
# Forward with throw an error if 'train_explainer_algorithm' is not called
explanation = explainer.forward(x...)

Open to feedback on updates to the interface.

TODOs

1. Add example in follow up PR.

[codecov]

Codecov Report

Merging #6204 (90ec896) into master (eb93ec0) will increase coverage by 0.03%.
The diff coverage is 91.20%.

❗ Current head 90ec896 differs from pull request most recent head 4688043. Consider uploading reports for the commit 4688043 to get more accurate results

@@            Coverage Diff             @@
##           master    #6204      +/-   ##
==========================================
+ Coverage   84.56%   84.59%   +0.03%     
==========================================
  Files         380      381       +1     
  Lines       21162    21264     +102     
==========================================
+ Hits        17896    17989      +93     
- Misses       3266     3275       +9     

Impacted Files	Coverage Δ
torch_geometric/explain/algorithm/base.py	93.82% <90.90%> (-1.18%)	⬇️
torch_geometric/explain/algorithm/pg_explainer.py	91.08% <91.08%> (ø)
torch_geometric/explain/algorithm/__init__.py	100.00% <100.00%> (ø)
torch_geometric/explain/algorithm/gnn_explainer.py	97.65% <100.00%> (+1.01%)	⬆️

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[rusty1s]

Thanks @wsad1. I made some slight change by moving the training loop outside of the explainer. I feel this allows the user more control, as the model might be training on a variety of graphs. Hope the changes are okay. Feel free to revise them if you feel they don't make sense.