code: def _ensure_sparse_format(
      spmatrix,
      accept_sparse,
      dtype,
      copy,
      force_all_finite,
      accept_large_sparse,
  ):
      """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, boolean or list/tuple of strings
          String[s] representing allowed sparse matrix formats ('csc',
          'csr', 'coo', 'dok', 'bsr', 'lil', 'dia'). If the input is sparse but
          not in the allowed format, it will be converted to the first listed
          format. True allows the input to be any format. False means
          that a sparse matrix input will raise an error.
  
      dtype : string, type or None
          Data type of result. If None, the dtype of the input is preserved.
  
      copy : boolean
          Whether a forced copy will be triggered. If copy=False, a copy might
          be triggered by a conversion.
  
      force_all_finite : boolean or 'allow-nan', (default=True)
          Whether to raise an error on np.inf and np.nan in X. The possibilities
          are:
  
          - True: Force all values of X to be finite.
          - False: accept both np.inf and np.nan in X.
          - 'allow-nan': accept only np.nan values in X. Values cannot be
            infinite.
  
      Returns
      -------
      spmatrix_converted : scipy sparse matrix.
          Matrix that is ensured to have an allowed type.
      """
      if dtype is None:
          dtype = spmatrix.dtype
  
      changed_format = False
  
      if isinstance(accept_sparse, str):
          accept_sparse = [accept_sparse]
  
      # Indices dtype validation
      # _check_large_sparse(spmatrix, accept_large_sparse)
  
      if accept_sparse is False:
          raise TypeError(
              "A sparse tensor was passed, but dense "
              "data is required. Use X.todense() to "
              "convert to a dense tensor."
          )
      elif isinstance(accept_sparse, (list, tuple)):
          if len(accept_sparse) == 0:
              raise ValueError(
                  "When providing 'accept_sparse' "
                  "as a tuple or list, it must contain at "
                  "least one string value."
              )
          # # ensure correct sparse format
          # if spmatrix.format not in accept_sparse:
          #     # create new with correct sparse
          #     spmatrix = spmatrix.asformat(accept_sparse[0])
          #     changed_format = True
      elif accept_sparse is not True:
          # any other type
          raise ValueError(
              "Parameter 'accept_sparse' should be a string, "
              "boolean or list of strings. You provided "
              f"'accept_sparse={accept_sparse}'."
          )
  
      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:
          spmatrix = assert_all_finite(
              spmatrix,
              allow_nan=force_all_finite == "allow-nan",
              check_only=False,
          )
  
      return spmatrix
  
imports: import warnings
  import numbers
  import numpy
  from numpy.core.numeric import ComplexWarning
  from ...None import dataframe
  from ...None import tensor
  from ...core import ExecutableTuple
  from ...lib.sparse import issparse
  from ...tensor import Tensor
  from .checks import check_non_negative_then_return_value, assert_all_finite, AssertAllFinite
label_desc: Use image node_id 6 for calling a global function with example usage: _ensure_sparse_format(spmatrix, accept_sparse, dtype, copy, force_all_finite, accept_large_sparse) and returns: spmatrix