Add e2e link prediction example with temporal information for the distributed solution

[kgajdamo]

Description:

This PR purpose is to add e2e link prediction example to run distributed training on a MovieLens dataset. It is characterized by the presence of temporal information for the edges.

This example is strongly inspired on: distributed_cpu.py and temporal_link_pred.py

IMPORTANT INFORMATION: This script depends on Enable distributed link hetero sampling PR. It will not work without the changes made in that PR. So that one should be merged first.

Script information:

• Link prediction
• Edge based temporal information
• MovieLens dataset

How to run:
The example should be evaluated on at least 2 machines. Before running the example a prework needs to be done. It is necessary to generate a partitions of the MovieLens dataset using a partition_graph.py script using the command below:
python partition_graph.py --MovieLens --num_partitions {number of machines}

Example commands to run the script:
Node 0:
python ./distributed_link_temporal_cpu.py --num_nodes 2 --node_rank 0 --batch_size 1024 --master_addr {ip address of one of the machines} --ddp_port 11111 --train_loader_port 11112 --test_loader_port 11113

Node 1:
python ./distributed_link_temporal_cpu.py --num_nodes 2 --node_rank 1 --batch_size 1024 --master_addr {ip address of one of the machines} --ddp_port 11111 --train_loader_port 11112 --test_loader_port 11113

(As you can see the only difference is the --node_rank argument.)

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

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Additional details and impacted files

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+ Coverage        0   89.28%   +89.28%     
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- Misses          0     3204     +3204     

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