code: def toarray(self, order=None, out=None):
      B = self._process_toarray_args(order, out)
      fortran = int(B.flags.f_contiguous)
      if not fortran and not B.flags.c_contiguous:
          raise ValueError("Output array must be C or F contiguous")
      if self.ndim > 2:
          raise ValueError("Cannot densify higher-rank sparse array")
      # This handles both 0D and 1D cases correctly regardless of the
      # original shape.
      M, N = self._shape_as_2d
      coo_todense(
          M,
          N,
          self.nnz,
          self.row,
          self.col,
          self.data,
          B.ravel("A"),
          fortran,
      )
      # Note: reshape() doesn't copy here, but does return a new array (view).
      return B.reshape(self.shape)
  
imports: import math
  from warnings import warn
  import numpy
  from ._matrix import spmatrix
  from ._sparsetools import coo_tocsr, coo_todense, coo_matvec
  from ._base import issparse, SparseEfficiencyWarning, _spbase, sparray
  from ._data import _data_matrix, _minmax_mixin
  from ._sputils import upcast, upcast_char, to_native, isshape, getdtype, getdata, downcast_intp_index, get_index_dtype, check_shape, check_reshape_kwargs
  import operator
label_desc: Use image node_id 11 for calling the _coo_base obj's underlying member method code with example usage: obj.toarray(order, out) and returns: B