class_docstr: Gradient of the matrix exponential of a square array.
class_docstr_tok: ["Gradient","of","the","matrix","exponential","of","a","square","array","."]
code: def make_node(self, A, gw):
      assert (
          imported_scipy
      ), "Scipy not available. Scipy is needed for the Expm op"
      A = as_tensor_variable(A)
      assert A.ndim == 2
      out = theano.tensor.matrix(dtype=A.dtype)
      return Apply(
          self,
          [A, gw],
          [
              out,
          ],
      )
  
imports: from __future__ import absolute_import, print_function, division
  import logging
  import warnings
  from six.moves import xrange
  import numpy
  from theano import tensor
  import theano.tensor
  from theano.tensor import as_tensor_variable
  from theano.gof import Op, Apply
label_desc: Use image node_id 1 for calling the ExpmGrad obj's underlying member method code with example usage: obj.make_node(A, gw) and returns: Apply