code: def test_all():
      for device in ["cpu", "gpu"]:
          for M_kind in [
              None,
              "vector",
              "scalar",
              "input",
              "scalar input",
          ]:
              for T_kind in [
                  None,
                  "vector",
                  "scalar",
                  "input",
                  "scalar input",
              ]:
                  for batch_size in [1, 3]:
                      yield (
                          _run_test,
                          device,
                          batch_size,
                          3,
                          3,
                          np.float32,
                          np.float32,
                          M_kind,
                          T_kind,
                      )
  
      for device in ["cpu", "gpu"]:
          for in_dtype in [
              np.uint8,
              np.uint16,
              np.int16,
              np.int32,
              np.float32,
          ]:
              for out_dtype in set([in_dtype, np.float32]):
                  for batch_size in [1, 8]:
                      yield (
                          _run_test,
                          device,
                          batch_size,
                          3,
                          3,
                          in_dtype,
                          out_dtype,
                          "input",
                          "input",
                      )
  
      for device in ["cpu", "gpu"]:
          for M_kind in ["input", "vector", None]:
              for in_dim in [1, 2, 3, 4, 5, 6]:
                  if M_kind == "vector" or M_kind == "input":
                      out_dims = [1, 2, 3, 4, 5, 6]
                  else:
                      out_dims = [in_dim]
                  for out_dim in out_dims:
                      yield (
                          _run_test,
                          device,
                          2,
                          out_dim,
                          in_dim,
                          np.float32,
                          np.float32,
                          M_kind,
                          "vector",
                      )
  
      for device in ["cpu", "gpu"]:
          for MT_kind in ["vector", "input", "scalar"]:
              for in_dim in [1, 2, 3, 4, 5, 6]:
                  if MT_kind == "vector" or MT_kind == "input":
                      out_dims = [1, 2, 3, 4, 5, 6]
                  else:
                      out_dims = [in_dim]
                  for out_dim in out_dims:
                      yield (
                          _run_test,
                          device,
                          2,
                          out_dim,
                          in_dim,
                          np.float32,
                          np.float32,
                          MT_kind,
                          "fused",
                      )
  
file_path: DALI/dali/test/python/operator_1/test_coord_transform.py
imports: import random
  import numpy
  import nvidia.dali
  import nvidia.dali.fn
  from test_utils import check_batch, dali_type
  from sequences_test_utils import ArgData, ArgDesc, sequence_suite_helper, ArgCb
label_desc: Use image node_id 6 for calling a global function with example usage: test_all() without return types