def convert_to_cvxpy(sdp):
from cvxpy import Minimize, Problem, Variable<EOL>row_offsets = [<NUM_LIT:0>]<EOL>cumulative_sum = <NUM_LIT:0><EOL>for block_size in sdp.block_struct:<EOL><INDENT>cumulative_sum += block_size ** <NUM_LIT:2><EOL>row_offsets.append(cumulative_sum)<EOL><DEDENT>x = Variable(sdp.n_vars)<EOL>constraints = []<EOL>for idx, bs in enumerate(sdp.block_struct):<EOL><INDENT>nonzero_set = set()<EOL>F = [lil_matrix((bs, bs)) for _ in range(sdp.n_vars+<NUM_LIT:1>)]<EOL>for ri, row in enumerate(sdp.F.rows[row_offsets[idx]:<EOL>row_offsets[idx+<NUM_LIT:1>]],<EOL>row_offsets[idx]):<EOL><INDENT>block_index, i, j = convert_row_to_sdpa_index(sdp.block_struct,<EOL>row_offsets, ri)<EOL>for col_index, k in enumerate(row):<EOL><INDENT>value = sdp.F.data[ri][col_index]<EOL>F[k][i, j] = value<EOL>F[k][j, i] = value<EOL>nonzero_set.add(k)<EOL><DEDENT><DEDENT>if bs > <NUM_LIT:1>:<EOL><INDENT>sum_ = sum(F[k]*x[k-<NUM_LIT:1>] for k in nonzero_set if k > <NUM_LIT:0>)<EOL>if not isinstance(sum_, (int, float)):<EOL><INDENT>if F[<NUM_LIT:0>].getnnz() > <NUM_LIT:0>:<EOL><INDENT>sum_ += F[<NUM_LIT:0>]<EOL><DEDENT>constraints.append(sum_ >> <NUM_LIT:0>)<EOL><DEDENT><DEDENT>else:<EOL><INDENT>sum_ = sum(F[k][<NUM_LIT:0>, <NUM_LIT:0>]*x[k-<NUM_LIT:1>] for k in nonzero_set if k > <NUM_LIT:0>)<EOL>if not isinstance(sum_, (int, float)):<EOL><INDENT>sum_ += F[<NUM_LIT:0>][<NUM_LIT:0>, <NUM_LIT:0>]<EOL>constraints.append(sum_ >= <NUM_LIT:0>)<EOL><DEDENT><DEDENT><DEDENT>obj = sum(ci*xi for ci, xi in zip(sdp.obj_facvar, x) if ci != <NUM_LIT:0>)<EOL>problem = Problem(Minimize(obj), constraints)<EOL>return problem<EOL>
Convert an SDP relaxation to a CVXPY problem.

    :param sdp: The SDP relaxation to convert.
    :type sdp: :class:`ncpol2sdpa.sdp`.

    :returns: :class:`cvxpy.Problem`.