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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def masked_mean(vector: torch.Tensor, mask: torch.Tensor, dim: int, keepdim: bool = False, eps: float = 1e-8) -> torch.Tensor: """ To calculate mean along certain dimensions on masked values Parameters vector : ``torch.Tensor`` The vector to calculate mean. mask : ``torch.Tensor`` The mask of the vector. It must be broadcastable with vector. dim : ``int`` The dimension to calculate mean keepdim : ``bool`` Whether to keep dimension eps : ``float`` A small value to avoid zero division problem. Returns ------- A ``torch.Tensor`` of including the mean values. """

    one_minus_mask = (1.0 - mask).byte()
    replaced_vector = vector.masked_fill(one_minus_mask, 0.0)

    value_sum = torch.sum(replaced_vector, dim=dim, keepdim=keepdim)
    value_count = torch.sum(mask.float(), dim=dim, keepdim=keepdim)
    return value_sum / value_count.clamp(min=eps)