Data Transform for fine-tuning (image & video datasets)

Hi, have anyone ever encountered issues with "transforms"? Whether fine-tuning on a video or image dataset, I don't quite understand the importance of each transform. It seems that the only essential ones (if removed, errors occur) are RandomResize, ToTensor, and Normalize. Can the others be omitted?
`vos:
train_transforms:
- _target_: training.dataset.transforms.ComposeAPI
transforms:
# - _target_: training.dataset.transforms.RandomHorizontalFlip
# consistent_transform: True
# - _target_: training.dataset.transforms.RandomAffine
# degrees: 25
# shear: 20
# image_interpolation: bilinear
# consistent_transform: True
- _target_: training.dataset.transforms.RandomResizeAPI
sizes: ${scratch.resolution}
square: true
consistent_transform: True
# - _target_: training.dataset.transforms.ColorJitter
# consistent_transform: True
# brightness: 0.1
# contrast: 0.03
# saturation: 0.03
# hue: null
# - _target_: training.dataset.transforms.RandomGrayscale
# p: 0.05
# consistent_transform: True
# - _target_: training.dataset.transforms.ColorJitter
# consistent_transform: False
# brightness: 0.1
# contrast: 0.05
# saturation: 0.05
# hue: null
- _target_: training.dataset.transforms.ToTensorAPI
- _target_: training.dataset.transforms.NormalizeAPI
mean: [0.485, 0.456, 0.406]
std: [0.229, 0.224, 0.225]`

The issue names the training.dataset.transforms entry points and a vos.train_transforms configuration, but no source file or test. Start by locating the listed transform implementations and their dataset/configuration usage; document what each transform does, which are required, and how to verify image and video fine-tuning still works.

Written by the indexing model from the issue text.