code: def inv(A):
      """
      Compute the inverse of a sparse matrix
  
      Parameters
      ----------
      A : (M, M) sparse matrix
          square matrix to be inverted
  
      Returns
      -------
      Ainv : (M, M) sparse matrix
          inverse of `A`
  
      Notes
      -----
      This computes the sparse inverse of `A`. If the inverse of `A` is expected
      to be non-sparse, it will likely be faster to convert `A` to dense and use
      `scipy.linalg.inv`.
  
      Examples
      --------
      >>> from scipy.sparse import csc_matrix
      >>> from scipy.sparse.linalg import inv
      >>> A = csc_matrix([[1., 0.], [1., 2.]])
      >>> Ainv = inv(A)
      >>> Ainv
      <2x2 sparse matrix of type '<class 'numpy.float64'>'
          with 3 stored elements in Compressed Sparse Column format>
      >>> A.dot(Ainv)
      <2x2 sparse matrix of type '<class 'numpy.float64'>'
          with 2 stored elements in Compressed Sparse Column format>
      >>> A.dot(Ainv).toarray()
      array([[ 1.,  0.],
             [ 0.,  1.]])
  
      .. versionadded:: 0.12.0
  
      """
      # Check input
      if not (scipy.sparse.issparse(A) or is_pydata_spmatrix(A)):
          raise TypeError("Input must be a sparse matrix")
  
      # Use sparse direct solver to solve "AX = I" accurately
      I = _ident_like(A)
      Ainv = spsolve(A, I)
      return Ainv
  
imports: import numpy
  from scipy.linalg._basic import solve, solve_triangular
  from scipy.sparse._base import issparse
  from scipy.sparse.linalg import spsolve
  from scipy.sparse._sputils import is_pydata_spmatrix, isintlike
  import scipy.sparse
  import scipy.sparse.linalg
  from scipy.sparse.linalg._interface import LinearOperator
  from scipy.sparse._construct import eye
  from ._expm_multiply import _ident_like, _exact_1_norm
label_desc: Use image node_id 1 for calling a global function with example usage: inv(A) and returns: Ainv