def METHOD_NAME(self, value, scores, mask):
    """Compute attention context vector.
    Args:
        value (torch.Tensor): Transformed value (#batch, n_head, time2, d_k).
        scores (torch.Tensor): Attention score (#batch, n_head, time1, time2).
        mask (torch.Tensor): Mask (#batch, 1, time2) or (#batch, time1, time2).
    Returns:
        torch.Tensor: Transformed value (#batch, time1, d_model)
            weighted by the attention score (#batch, time1, time2).
    """
    n_batch = value.size(0)
    if mask is not None:
        mask = mask.unsqueeze(1).eq(0)  # (batch, 1, *, time2)
        min_value = float(
            numpy.finfo(torch.tensor(0, dtype=scores.dtype).numpy().dtype).min
        )
        scores = scores.masked_fill(mask, min_value)
        self.attn = torch.softmax(scores, dim=-1).masked_fill(
            mask, 0.0
        )  # (batch, head, time1, time2)
    else:
        self.attn = torch.softmax(scores, dim=-1)  # (batch, head, time1, time2)
    p_attn = self.dropout(self.attn)
    x = torch.matmul(p_attn, value)  # (batch, head, time1, d_k)
    x = (
        x.transpose(1, 2).contiguous().view(n_batch, -1, self.h * self.d_k)
    )  # (batch, time1, d_model)
    return self.linear_out(x)  # (batch, time1, d_model)