code: def test_torch_atleast_1d(
      *,
      dtype_and_tensors,
      on_device,
      fn_tree,
      frontend,
      test_flags,
      backend_fw,
  ):
      dtypes, tensors = dtype_and_tensors
      if isinstance(
          dtypes, list
      ):  # If more than one value was generated
          args = {
              f"x{i}": np.array(tensor, dtype=dtypes[i])
              for i, tensor in enumerate(tensors)
          }
      else:  # If exactly one value was generated
          args = {"x0": np.array(tensors, dtype=dtypes)}
      test_flags.num_positional_args = len(tensors)
      helpers.test_frontend_function(
          input_dtypes=dtypes,
          backend_to_test=backend_fw,
          frontend=frontend,
          test_flags=test_flags,
          fn_tree=fn_tree,
          on_device=on_device,
          **args,
      )
  
imports: import math
  import numpy
  from hypothesis import assume, strategies
  import hypothesis.extra.numpy
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_core.test_linalg import _get_dtype_value1_value2_axis_for_tensordot
label_desc: Use image node_id 7 for calling a global function with example usage: test_torch_atleast_1d() without return types