code: def mean_variance_axis(X, axis):
      """Compute mean and variance along an axix on a CSR or CSC matrix
  
      Parameters
      ----------
      X: CSR or CSC sparse matrix, shape (n_samples, n_features)
          Input data.
  
      axis: int (either 0 or 1)
          Axis along which the axis should be computed.
  
      Returns
      -------
  
      means: float array with shape (n_features,)
          Feature-wise means
  
      variances: float array with shape (n_features,)
          Feature-wise variances
  
      """
      _raise_error_wrong_axis(axis)
  
      if isinstance(X, sp.csr_matrix):
          if axis == 0:
              return _csr_mean_var_axis0(X)
          else:
              return _csc_mean_var_axis0(X.T)
      elif isinstance(X, sp.csc_matrix):
          if axis == 0:
              return _csc_mean_var_axis0(X)
          else:
              return _csr_mean_var_axis0(X.T)
      else:
          _raise_typeerror(X)
  
imports: import scipy.sparse
  import numpy
  from .fixes import sparse_min_max, bincount
  from .sparsefuncs_fast import csr_mean_variance_axis0, csc_mean_variance_axis0, incr_mean_variance_axis0
label_desc: Use image node_id 5 for calling a global function with example usage: mean_variance_axis(X, axis) and returns: _csr_mean_var_axis0, _csc_mean_var_axis0, _csc_mean_var_axis0, _csr_mean_var_axis0