code: def test_matmul_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)
  
      linop1 = MyLinOp((1, 3, 1, 2))
      linop2 = MyLinOp((1, 3, 2, 1))
      linop_result = linop1.matmul(linop2)
      x = torch.rand(1, 3, 1, 1)
      result = linop_result.mv(x)
      assert result.shape == torch.Size([1, 3, 1, 1])
  
imports: import pytest
label_desc: Use image node_id 19 for calling a global function with example usage: test_matmul_linear_operator() and returns: list, torch