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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 random_stochastic_matrix(n, k=None, sparse=False, format='csr', random_state=None):
 """ Return a randomly sampled n x n stochastic matrix with k nonzero entries for each row. Parameters n : scalar(int) Number of states. k : scalar(int), optional(default=None) Number of nonzero entries in each row of the matrix. Set to n if not specified. sparse : bool, optional(default=False) Whether to generate the matrix in sparse matrix form. format : str, optional(default='csr') Sparse matrix format, str in {'bsr', 'csr', 'csc', 'coo', 'lil', 'dia', 'dok'}. Relevant only when sparse=True. random_state : int or np.random.RandomState, optional Random seed (integer) or np.random.RandomState instance to set the initial state of the random number generator for reproducibility. If None, a randomly initialized RandomState is used. Returns ------- P : numpy ndarray or scipy sparse matrix (float, ndim=2) Stochastic matrix. See also -------- random_markov_chain : Return a random MarkovChain instance. """

    P = _random_stochastic_matrix(m=n, n=n, k=k, sparse=sparse, format=format,
                                  random_state=random_state)
    return P