Resolves #45.
Related to #193, #205, #215.

Description
This transform is useful for situations in which we don't mind changing the aspect ratio of the object and want to keep the field of view constant. This happens often in computer vision applications. For example, I am using this transform on videos.

In [1]: !curl -o person.jpg https://thispersondoesnotexist.com/image
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  398k  100  398k    0     0  1726k      0 --:--:-- --:--:-- --:--:-- 1726k

In [4]: im = tio.ScalarImage('person.jpg')

In [5]: im.plot()

In [6]: im.shape
Out[6]: (3, 1024, 1024, 1)

In [7]: im = tio.Crop((100, 0, 0))(im)  # simulate a rectangular image

In [8]: im.shape
Out[8]: (3, 824, 1024, 1)

In [9]: im.plot()

In [13]: resize = tio.Resize((224, 224, 1))

In [14]: resized = resize(im)

In [15]: resized.shape
Out[15]: (3, 224, 224, 1)

In [16]: resized.plot()

Checklist

• I have read the CONTRIBUTING docs and have a developer setup (especially important are pre-commitand pytest)
• Non-breaking change (would not break existing functionality)
• Breaking change (would cause existing functionality to change)
• Tests added or modified to cover the changes
• Integration tests passed locally by running pytest
• In-line docstrings updated
• Documentation updated, tested running make html inside the docs/ folder
• This pull request is ready to be reviewed
• If the PR is ready and there are multiple commits, I have squashed them and force-pushed