Add batch visualization function to `torchvision.utils`

🚀 The feature

Currently vision models commonly return dictionary

model = fasterrcnn_resnet50_fpn_v2(
    weights=FasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1,
)
dog_int = io.read_image("dog.jpeg")
batch = f.convert_image_dtype(dog_int)
response = model(batch.unsqueeze(0))
# [{'boxes': tensor([[ 61.9160,  49.2223, 185.7204, 184.4109],
#          [143.5477, 143.5992, 175.1468, 184.4712],
#          [136.6003, 217.3529, 161.4755, 223.8254]]),
#  'labels': tensor([18, 20, 57]),
#  'scores': tensor([0.9989, 0.1069, 0.0699])}]

This is visualized by torchvision by

dog_image = utils.draw_bounding_boxes(
    dog_int, response[0]["boxes"][response[0]["scores"] > 0.3]
)
f.to_pil_image(dog_image)

Motivation, pitch

However, there is no currenly a way to visualize whole batch with a function in utilities, eg.
continuing from previous

with torch.no_grad():
    imgs = [img.detach().clone() for _ in range(16)]
    model.eval()
    response = model(imgs)
# The input is float tensor batch
# output is equal length list of output dictionaries

There is no currenly available method to visualize this batch for user.

I suggest an utils function

def visualize_batch(image_batch: Tensor | list[Tensor], batch_response: list[dict[str, Tensor]], **visualization_arguments):
    """Function visualizes image batch in a suitable grid and returns result as a tensor

       Arguments:
            image_batch (Tensor): Float tensor batch, internally transform to uint8 for visualization utils
            batch_response: List of response dictionaries, works for both mask and rcnn models as well as training batches by inferring correct behavior from dictionary keys.
    """
    # ... implementation

where keyword arguments relate to arguments in current utils functions.

Alternatives

User could follow utilities example at example which implements matplotlib function show. This is non-ideal, as users seems to have need for similar functionality without related boilderplate code.

Additional context

I am willing to contribute given green light.

Start by reading the existing torchvision.utils visualization functions and the linked visualization example. Compare their arguments with the proposed visualize_batch inputs, then clarify supported response dictionaries, grid behavior, and how completion will be verified.