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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 single_lf_summary(Y_p, Y=None):
 """Calculates coverage, overlap, conflicts, and accuracy for a single LF Args: Y_p: a np.array or torch.Tensor of predicted labels Y: a np.array or torch.Tensor of true labels (if known) """

    L = sparse.csr_matrix(arraylike_to_numpy(Y_p).reshape(-1, 1))
    return lf_summary(L, Y)