code: def test_array_derivative_construction():
      d = ArrayDerivative(x, m, evaluate=False)
      assert d.shape == (2, 2)
      expr = d.doit()
      assert isinstance(expr, MatrixBase)
      assert expr.shape == (2, 2)
  
      d = ArrayDerivative(m, m, evaluate=False)
      assert d.shape == (2, 2, 2, 2)
      expr = d.doit()
      assert isinstance(expr, NDimArray)
      assert expr.shape == (2, 2, 2, 2)
  
      d = ArrayDerivative(m, x, evaluate=False)
      assert d.shape == (2, 2)
      expr = d.doit()
      assert isinstance(expr, MatrixBase)
      assert expr.shape == (2, 2)
  
      d = ArrayDerivative(M, N, evaluate=False)
      assert d.shape == (4, 3, 3, 2)
      expr = d.doit()
      assert isinstance(expr, ArrayDerivative)
      assert expr.shape == (4, 3, 3, 2)
  
      d = ArrayDerivative(M, (N, 2), evaluate=False)
      assert d.shape == (4, 3, 4, 3, 3, 2)
      expr = d.doit()
      assert isinstance(expr, ArrayDerivative)
      assert expr.shape == (4, 3, 4, 3, 3, 2)
  
      d = ArrayDerivative(
          M.as_explicit(), (N.as_explicit(), 2), evaluate=False
      )
      assert d.doit().shape == (4, 3, 4, 3, 3, 2)
      expr = d.doit()
      assert isinstance(expr, NDimArray)
      assert expr.shape == (4, 3, 4, 3, 3, 2)
  
imports: from sympy.core.symbol import symbols
  from sympy.matrices.dense import Matrix
  from sympy.matrices.expressions.matexpr import MatrixSymbol
  from sympy.tensor.array.ndim_array import NDimArray
  from sympy.matrices.matrixbase import MatrixBase
  from sympy.tensor.array.array_derivatives import ArrayDerivative
label_desc: Use image node_id 1 for calling a global function with example usage: test_array_derivative_construction() without return types