class_docstr: Tests keywords, multiple arguments, and non-training arguments in relevant optimizers
docstr: Tests whether separating the args into different inputs affects their
  optimization step. Assumes single argument optimization is correct, as tested elsewhere.
docstr_tok: ["Tests","whether","separating","the","args","into","different","inputs","affects","their","optimization","step",".","Assumes","single","argument","optimization","is","correct",",","as","tested","elsewhere","."]
code: def test_steps_the_same(self, opt, tol):
      """Tests whether separating the args into different inputs affects their
      optimization step. Assumes single argument optimization is correct, as tested elsewhere.
      """
  
      def func1(x, y, z):
          return x[0] * y[0] * z[0]
  
      def func2(args):
          return args[0][0] * args[1][0] * args[2][0]
  
      x = np.array([1.0], requires_grad=True)
      y = np.array([2.0], requires_grad=True)
      z = np.array([3.0], requires_grad=True)
      args = np.array((x, y, z), requires_grad=True)
  
      x_seperate, y_seperate, z_seperate = opt.step(func1, x, y, z)
      self.reset(opt)
  
      args_new = opt.step(func2, args)
      self.reset(opt)
  
      assert np.allclose(x_seperate, args_new[0], atol=tol)
      assert np.allclose(y_seperate, args_new[1], atol=tol)
      assert np.allclose(z_seperate, args_new[2], atol=tol)
  
file_path: pennylane/tests/optimize/test_optimize.py
imports: import pytest
  import pennylane
  from pennylane import numpy
  from pennylane.optimize import GradientDescentOptimizer, MomentumOptimizer, NesterovMomentumOptimizer, AdagradOptimizer, RMSPropOptimizer, AdamOptimizer
label_desc: Use image node_id 4 for calling the TestOverOpts obj's underlying member method code with example usage: obj.test_steps_the_same(opt, tol) and returns: unknown, unknown