code: def test_solve(real_vec):
      if is_torch_1_9_plus:
          wrappers = [ComplexTensor, torch.complex]
          modules = [FC, torch]
      else:
          wrappers = [ComplexTensor]
          modules = [FC]
  
      for complex_wrapper, complex_module in zip(wrappers, modules):
          mat = complex_wrapper(
              torch.from_numpy(mat_np.real),
              torch.from_numpy(mat_np.imag),
          )
          if not real_vec or complex_wrapper is ComplexTensor:
              vec = complex_wrapper(
                  torch.rand(2, 3, 1), torch.rand(2, 3, 1)
              )
              vec2 = vec
          else:
              vec = torch.rand(2, 3, 1)
              vec2 = complex_wrapper(vec, torch.zeros_like(vec))
          ret = solve(vec, mat)
          if isinstance(vec2, ComplexTensor):
              ret2 = FC.solve(vec2, mat, return_LU=False)
          else:
              return torch.linalg.solve(mat, vec2)
          assert complex_module.allclose(ret, ret2)
  
file_path: espnet/test/espnet2/enh/layers/test_complex_utils.py
imports: import numpy
  import pytest
  import torch
  import torch_complex.functional
  from packaging.version import parse
  from torch_complex.tensor import ComplexTensor
  from espnet2.enh.layers.complex_utils import cat, complex_norm, einsum, inverse, matmul, solve, stack, trace
label_desc: Use image node_id 7 for calling a global function with example usage: test_solve(real_vec) and returns: torch