class_docstr: Identity vector function and its derivatives.
  
  The Jacobian is the identity matrix, returned as a dense array when
  `sparse_jacobian=False` and as a csr matrix otherwise. The Hessian is
  identically zero and it is returned as a csr matrix.
class_docstr_tok: ["Identity","vector","function","and","its","derivatives",".","The","Jacobian","is","the","identity","matrix",",","returned","as","a","dense","array","when","`","sparse_jacobian=False","`","and","as","a","csr","matrix","otherwise",".","The","Hessian","is","identically","zero","and","it","is","returned","as","a","csr","matrix","."]
code: def __init__(self, x0, sparse_jacobian):
      n = len(x0)
      if sparse_jacobian or sparse_jacobian is None:
          A = sps.eye(n, format="csr")
          sparse_jacobian = True
      else:
          A = np.eye(n)
          sparse_jacobian = False
      super(IdentityVectorFunction, self).__init__(
          A, x0, sparse_jacobian
      )
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import scipy.sparse
  from ._numdiff import approx_derivative, group_columns
  from ._hessian_update_strategy import HessianUpdateStrategy
  from scipy.sparse.linalg import LinearOperator
label_desc: Use image node_id 1 to create a new IdentityVectorFunction object from inherited base classes: LinearVectorFunction with example: obj = IdentityVectorFunction(x0, sparse_jacobian)