Doctests for RMSE and MeanPairwiseDistance

[DevPranjal]

Addresses #2265

Description: Added doctests for RMSE and MeanPairwiseDistance

Check list:

• New tests are added (if a new feature is added)
• New doc strings: description and/or example code are in RST format
• Documentation is updated (if required)

[DevPranjal]

I have added a global for the default evaluator to which @vfdev-5 was referring to in this comment.
I also felt we could mention it somewhere... What could be a right place to mention it, if necessary?
Thanks :)

[sdesrozis]

@DevPranjal Thank you. I think we already discussed that point, and the idea is to have simple and copy/paste pieces of code, even if a lot of code could be reused.

@vfdev-5 what do you think ?

[vfdev-5]

@sdesrozis I do not see the point to make API examples cluttered with other "unrealistic" code just to make them executable.
We can however publicly display doctest_global_setup to users such that they could reuse it if needed reproducible code

[sdesrozis]

The point is the user can't copy/paste the code to test. I thought we would like to provide users with easily reproducible examples.

Anyway, let's choose whether we hide things and let's deploy 😊

[vfdev-5]

I know, but it means for this metric we'll have the following:

import torch
from torch import nn, optim
from ignite.engine import *
from ignite.handlers import *
from ignite.metrics import *
from ignite.utils import *

manual_seed(666)

def process_function(engine, batch):
    y_pred, y = batch
    return y_pred, y

default_evaluator = Engine(process_function)

# --- API example 
metric = MeanPairwiseDistance(p=4)
metric.attach(default_evaluator, 'mpd')
preds = torch.Tensor([
                [1, 2, 4, 1],
                [2, 3, 1, 5],
                [1, 3, 5, 1],
                [1, 5, 1 ,11]
])
target = preds * 0.75
state = default_evaluator.run([[preds, target]])
print(state.metrics['mpd'])

You have half of the code snippet of preps.

[sdesrozis]

We are talking about more or less 30 code lines, and using lambda function, we could reduce this. Not sure that it's worth hiding rather than exposing.

Anyway, that's a matter of taste.

[vfdev-5]

LGTM, thanks @DevPranjal !