code: def test_eval_partial_derivative_single_tensors_by_scalar():
      tau, mu = symbols("tau mu")
  
      expr = PartialDerivative(tau**mu, tau)
      assert expr._perform_derivative() == mu * tau**mu / tau
  
      expr1a = PartialDerivative(A(i), tau)
      assert expr1a._perform_derivative() == 0
  
      expr1b = PartialDerivative(A(-i), tau)
      assert expr1b._perform_derivative() == 0
  
      expr2a = PartialDerivative(H(i, j), tau)
      assert expr2a._perform_derivative() == 0
  
      expr2b = PartialDerivative(H(i, -j), tau)
      assert expr2b._perform_derivative() == 0
  
      expr2c = PartialDerivative(H(-i, j), tau)
      assert expr2c._perform_derivative() == 0
  
      expr2d = PartialDerivative(H(-i, -j), tau)
      assert expr2d._perform_derivative() == 0
  
imports: from sympy import sin, cos
  from sympy.testing.pytest import raises
  from sympy.tensor.toperators import PartialDerivative
  from sympy.tensor.tensor import TensorIndexType, tensor_indices, TensorHead, tensor_heads
  from sympy.core.numbers import Rational
  from sympy.core.symbol import symbols
  from sympy.matrices.dense import diag
  from sympy.tensor.array import Array
  from sympy.core.random import randint
label_desc: Use image node_id 9 for calling a global function with example usage: test_eval_partial_derivative_single_tensors_by_scalar() without return types