src/utils.py [273:284]:
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    weights = torch.ones(mask_input.shape, dtype=torch.float32).to(pos_input3.device)
    if use_predicted_mask:
        weights[mask_input > 0.5] *= weight
    else:
        weights[mask[:, ::3]] *= weight
    weights = torch_tile(weights, dim=1, n_tile=3)
    weights = weights.reshape_as(target3)
    finger_weights = torch.ones_like(weights)
    finger_weights[:, l_hand_wo_wrist] *= finger_weight
    finger_weights[:, r_hand_wo_wrist] *= finger_weight
    pos_loss *= weights
    pos_loss *= finger_weights
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src/utils.py [432:443]:
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    weights = torch.ones(mask_input.shape, dtype=torch.float32).to(pos_input3.device)
    if use_predicted_mask:
        weights[mask_input > 0.5] *= weight
    else:
        weights[mask[:, ::3]] *= weight
    weights = torch_tile(weights, dim=1, n_tile=3)
    weights = weights.reshape_as(target3)
    finger_weights = torch.ones_like(weights)
    finger_weights[:, l_hand_wo_wrist] *= finger_weight
    finger_weights[:, r_hand_wo_wrist] *= finger_weight
    pos_loss *= weights
    pos_loss *= finger_weights
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