code: def _onenormest_product(
      operator_seq,
      t=2,
      itmax=5,
      compute_v=False,
      compute_w=False,
      structure=None,
  ):
      """
      Efficiently estimate the 1-norm of the matrix product of the args.
  
      Parameters
      ----------
      operator_seq : linear operator sequence
          Matrices whose 1-norm of product is to be computed.
      t : int, optional
          A positive parameter controlling the tradeoff between
          accuracy versus time and memory usage.
          Larger values take longer and use more memory
          but give more accurate output.
      itmax : int, optional
          Use at most this many iterations.
      compute_v : bool, optional
          Request a norm-maximizing linear operator input vector if True.
      compute_w : bool, optional
          Request a norm-maximizing linear operator output vector if True.
      structure : str, optional
          A string describing the structure of all operators.
          Only `upper_triangular` is currently supported.
  
      Returns
      -------
      est : float
          An underestimate of the 1-norm of the sparse matrix.
      v : ndarray, optional
          The vector such that ||Av||_1 == est*||v||_1.
          It can be thought of as an input to the linear operator
          that gives an output with particularly large norm.
      w : ndarray, optional
          The vector Av which has relatively large 1-norm.
          It can be thought of as an output of the linear operator
          that is relatively large in norm compared to the input.
  
      """
      return scipy.sparse.linalg.onenormest(
          ProductOperator(*operator_seq, structure=structure)
      )
  
imports: import numpy
  from scipy.linalg._basic import solve, solve_triangular
  from scipy.sparse._base import issparse
  from scipy.sparse.linalg import spsolve
  from scipy.sparse._sputils import is_pydata_spmatrix, isintlike
  import scipy.sparse
  import scipy.sparse.linalg
  from scipy.sparse.linalg._interface import LinearOperator
  from scipy.sparse._construct import eye
  from ._expm_multiply import _ident_like, _exact_1_norm
label_desc: Use image node_id 6 for calling a global function with example usage: _onenormest_product(operator_seq, t, itmax, compute_v, compute_w, structure) and returns: scipy