def METHOD_NAME(tensor, rank, svd="truncated_svd", verbose=False):
    """Decompose a tensor into a matrix in tt-format
    Parameters
    ----------
    tensor : tensorized matrix
        if your input matrix is of size (4, 9) and your tensorized_shape (2, 2, 3, 3)
        then tensor should be tl.reshape(matrix, (2, 2, 3, 3))
    rank : 'same', float or int tuple
        - if 'same' creates a decomposition with the same number of parameters as `tensor`
        - if float, creates a decomposition with `rank` x the number of parameters of `tensor`
        - otherwise, the actual rank to be used, e.g. (1, rank_2, ..., 1) of size tensor.ndim//2. Note that boundary conditions dictate that the first rank = last rank = 1.
    svd : str, default is 'truncated_svd'
        function to use to compute the SVD, acceptable values in tensorly.SVD_FUNS
    verbose : boolean, optional
            level of verbosity
    Returns
    -------
    tt_matrix
    """
    order = tl.ndim(tensor)
    n_input = order // 2  # (n_output = n_input)
    if tl.ndim(tensor) != n_input * 2:
        msg = "The tensor should have as many dimensions for inputs and outputs, i.e. order should be even "
        msg += f"but got a tensor of order tl.ndim(tensor)={order} which is odd."
        raise ValueError(msg)
    in_shape = tl.shape(tensor)[:n_input]
    out_shape = tl.shape(tensor)[n_input:]
    if n_input == 1:
        # A TTM with a single factor is just a matrix...
        return TTMatrix([tensor.reshape(1, in_shape[0], out_shape[0], 1)])
    new_idx = list(
        [
            idx
            for tuple_ in zip(range(n_input), range(n_input, 2 * n_input))
            for idx in tuple_
        ]
    )
    new_shape = list([a * b for (a, b) in zip(in_shape, out_shape)])
    tensor = tl.reshape(tl.transpose(tensor, new_idx), new_shape)
    factors = tensor_train(tensor, rank, svd=svd, verbose=verbose).factors
    for i in range(len(factors)):
        factors[i] = tl.reshape(
            factors[i], (factors[i].shape[0], in_shape[i], out_shape[i], -1)
        )
    return TTMatrix(factors)