[ADD] Optimise on custom metric

[ravinkohli]

This PR enables the user to add their own custom metric.

Types of changes

• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)

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Checklist:

• My code follows the code style of this project.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.

• Have you checked to ensure there aren't other open Pull Requests for the same update/change?
• Have you added an explanation of what your changes do and why you'd like us to include them?
• Have you written new tests for your core changes, as applicable?
• Have you successfully ran tests with your changes locally?

Description

This PR adds a function, add_metric which allows the user to pass any custom metric and make it available to AutoPyTorch for HPO. It also includes an example on how to use this functionality.

Motivation and Context

Fixes #477

How has this been tested?

I have added a test which checks if the given metric is available in the metrics for the current task type.

[codecov]

Codecov Report

Base: 85.28% // Head: 28.03% // Decreases project coverage by -57.25% ⚠️

Coverage data is based on head (1b1f07b) compared to base (17cdc09).
Patch coverage: 40.00% of modified lines in pull request are covered.

Additional details and impacted files

@@               Coverage Diff                @@
##           development     #486       +/-   ##
================================================
- Coverage        85.28%   28.03%   -57.26%     
================================================
  Files              232      232               
  Lines            16456    16467       +11     
  Branches          3048     2733      -315     
================================================
- Hits             14034     4616     -9418     
- Misses            1573    11849    +10276     
+ Partials           849        2      -847     

Impacted Files	Coverage Δ
...tup/traditional_ml/traditional_learner/learners.py	33.55% <0.00%> (-47.66%)	⬇️
...orch/pipeline/components/training/metrics/utils.py	11.11% <12.50%> (-76.89%)	⬇️
...ch/pipeline/components/training/metrics/metrics.py	76.47% <100.00%> (-12.77%)	⬇️
...tup/network_backbone/forecasting_backbone/cells.py	8.56% <0.00%> (-83.80%)	⬇️
...cessing/time_series_preprocessing/scaling/utils.py	8.91% <0.00%> (-83.17%)	⬇️
...mponents/setup/forecasting_target_scaling/utils.py	8.42% <0.00%> (-83.16%)	⬇️
...omponents/setup/network_backbone/ResNetBackbone.py	19.62% <0.00%> (-80.38%)	⬇️
...mponents/setup/network/forecasting_architecture.py	10.08% <0.00%> (-79.47%)	⬇️
...twork_head/forecasting_network_head/NBEATS_head.py	18.84% <0.00%> (-78.27%)	⬇️
autoPyTorch/datasets/time_series_dataset.py	12.89% <0.00%> (-77.74%)	⬇️
... and 209 more

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[dengdifan]

For Time Series Tasks, an additional kwargs must be attached:
https://github.com/sktime/sktime/blob/main/sktime/performance_metrics/forecasting/_functions.py#L179

Should we create a new example for this, or do we add another docstring here?

[ravinkohli]

I'll add it to the same example. We can divide this example into two parts- for tabular tasks and for time series tasks