code: def min_max_axis(X, axis):
      """Compute minimum and maximum along an axis 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
      -------
  
      mins: float array with shape (n_features,)
          Feature-wise minima
  
      maxs: float array with shape (n_features,)
          Feature-wise maxima
      """
      if isinstance(X, sp.csr_matrix) or isinstance(X, sp.csc_matrix):
          return sparse_min_max(X, axis=axis)
      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 13 for calling a global function with example usage: min_max_axis(X, axis) and returns: sparse_min_max