def get_sim_for_each_question(model, pooled_image_feat, pooled_text_feat):
    """TODO: Docstring for get_sim_for_each_question.

    Args:
        model (TODO): TODO
        pooled_image_feat (torch.Tensor): Shape: [b,t, c]
        pooled_text_feat (torch.Tensor): Shape: [b, n, c]. n is the number of answer candidates.

    Returns: TODO

    """
    image_proj = model.vision_proj
    text_proj = model.text_proj

    image_feat = F.normalize(image_proj(pooled_image_feat), dim=-1)
    text_feat = F.normalize(text_proj(pooled_text_feat), dim=-1)
    sim = torch.matmul(image_feat, rearrange(text_feat, "b n c -> b c n"))  # [b, t, n]
    sim = sim.mean(1) / model.temp  # [b,n]
    sim = F.softmax(sim, dim=1)  # [b, n]
    return sim
TODO: Docstring for get_sim_for_each_question.

Args:
model (TODO): TODO
pooled_image_feat (torch.Tensor): Shape: [b,t, c]
pooled_text_feat (torch.Tensor): Shape: [b, n, c]. n is the number of answer candidates.

Returns: TODO
videomamba/video_mm/tasks/retrieval_mc.py