code: def test_adjoint_linear_operator():
      from deepchem.utils.differentiation_utils import LinearOperator
  
      class MyLinOp(LinearOperator):
          def __init__(self, shape):
              super(MyLinOp, self).__init__(shape)
              self.param = torch.rand(shape)
  
          def _getparamnames(self, prefix=""):
              return [prefix + "param"]
  
          def _mv(self, x):
              return torch.matmul(self.param, x)
  
          def _rmv(self, x):
              return torch.matmul(
                  self.param.transpose(-2, -1).conj(), x
              )
  
      linop = MyLinOp((1, 3, 1, 2))
      x = torch.rand(1, 3, 1, 1)
      result_rmv = linop.rmv(x)
  
      adjoint_linop = linop.H
      result_mv = adjoint_linop.mv(x)
  
      assert torch.allclose(result_rmv, result_mv)
  
imports: import pytest
label_desc: Use image node_id 18 for calling a global function with example usage: test_adjoint_linear_operator() and returns: list, torch, torch