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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 tmatrix_cov(C, k=None):
 r"""Covariance tensor for non-reversible transition matrix posterior. Parameters C : (M, M) ndarray or scipy.sparse matrix Count matrix k : int (optional) Return only covariance matrix for entires in the k-th row of the transition matrix Returns ------- cov : (M, M, M) ndarray Covariance tensor for transition matrix posterior Notes ----- The posterior of non-reversible transition matrices is .. math:: \mathbb{P}(T|C) \propto \prod_{i=1}^{M} \left( \prod_{j=1}^{M} p_{ij}^{c_{ij}} \right) Each row in the transition matrix is distributed according to a Dirichlet distribution with parameters given by the observed transition counts :math:`c_{ij}`. The covariance tensor :math:`\text{cov}[p_{ij},p_{kl}]=\Sigma_{i,j,k,l}` is zero whenever :math:`i \neq k` so that only :math:`\Sigma_{i,j,i,l}` is returned. """

    if issparse(C):
        warnings.warn("Covariance matrix will be dense for sparse input")
        C = C.toarray()
    return dense.covariance.tmatrix_cov(C, row=k)