def ssnt_loss_mem(
    log_probs: Tensor,
    targets: Tensor,
    source_lengths: Tensor,
    target_lengths: Tensor,
    emit_logits: Optional[Tensor] = None,
    emit_probs: Optional[Tensor] = None,
    neg_inf: float = -1e4,
    reduction="none",
    fastemit_lambda=0
):
    """The memory efficient implementation concatenates along the targets
    dimension to reduce wasted computation on padding positions.

    N is the minibatch size
    T is the maximum number of output labels
    S is the maximum number of input frames
    V is the vocabulary of labels.
    T_flat is the summation of lengths of all output labels

    Assuming the original tensor is of (N, T, ...), then it should be reduced to
    (T_flat, ...). This can be obtained by using a target mask.
    For example:
        >>> target_mask = targets.ne(pad)   # (B, T)
        >>> targets = targets[target_mask]  # (T_flat,)
        >>> log_probs = log_probs[target_mask]  # (T_flat, S, V)

    Args:
        log_probs (Tensor): (T_flat, S, V) Word prediction log-probs, should be output of log_softmax.
        targets (Tensor): (T_flat,) target labels for all samples in the minibatch.
        source_lengths (Tensor): (N,) Length of the source frames for each sample in the minibatch.
        target_lengths (Tensor): (N,) Length of the target labels for each sample in the minibatch.
        emit_logits, emit_probs (Tensor, optional): (T_flat, S) Emission logits (before sigmoid) or
            probs (after sigmoid). If both are provided, logits is used.
        neg_inf (float, optional): The constant representing -inf used for masking.
            Default: -1e4
        reduction (string, optional): Specifies reduction. suppoerts mean / sum.
            Default: None.
        fastemit_lambda (float, optional): Scale the emission gradient of emission paths to
            encourage low latency. https://arxiv.org/pdf/2010.11148.pdf
            Default: 0
    """