code: def _ensure_sparse_format(
      spmatrix, accept_sparse, dtype, copy, force_all_finite
  ):
      """Convert a sparse matrix to a given format.
  
      Checks the sparse format of spmatrix and converts if necessary.
  
      Parameters
      ----------
      spmatrix : scipy sparse matrix
          Input to validate and convert.
  
      accept_sparse : string, list of string or None (default=None)
          String[s] representing allowed sparse matrix formats ('csc',
          'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). None means that sparse
          matrix input will raise an error.  If the input is sparse but not in
          the allowed format, it will be converted to the first listed format.
  
      dtype : string, type or None (default=none)
          Data type of result. If None, the dtype of the input is preserved.
  
      copy : boolean (default=False)
          Whether a forced copy will be triggered. If copy=False, a copy might
          be triggered by a conversion.
  
      force_all_finite : boolean (default=True)
          Whether to raise an error on np.inf and np.nan in X.
  
      Returns
      -------
      spmatrix_converted : scipy sparse matrix.
          Matrix that is ensured to have an allowed type.
      """
      if accept_sparse in [None, False]:
          raise TypeError(
              "A sparse matrix was passed, but dense "
              "data is required. Use X.toarray() to "
              "convert to a dense numpy array."
          )
      if dtype is None:
          dtype = spmatrix.dtype
  
      changed_format = False
      if (
          isinstance(accept_sparse, (list, tuple))
          and spmatrix.format not in accept_sparse
      ):
          # create new with correct sparse
          spmatrix = spmatrix.asformat(accept_sparse[0])
          changed_format = True
  
      if dtype != spmatrix.dtype:
          # convert dtype
          spmatrix = spmatrix.astype(dtype)
      elif copy and not changed_format:
          # force copy
          spmatrix = spmatrix.copy()
  
      if force_all_finite:
          if not hasattr(spmatrix, "data"):
              warnings.warn(
                  "Can't check %s sparse matrix for nan or inf."
                  % spmatrix.format
              )
          else:
              _assert_all_finite(spmatrix.data)
      return spmatrix
  
imports: import warnings
  import numbers
  import numpy
  import scipy.sparse
  from ..externals import six
  from ..utils.fixes import signature
  from .deprecation import deprecated
  from ..exceptions import DataConversionWarning
  from ..exceptions import NonBLASDotWarning
  from ..exceptions import NotFittedError
label_desc: Use image node_id 9 for calling a global function with example usage: _ensure_sparse_format(spmatrix, accept_sparse, dtype, copy, force_all_finite) and returns: spmatrix