code: def test_dropout1d(
      *,
      dtype_and_x,
      prob,
      training,
      data_format,
      test_flags,
      backend_fw,
      on_device,
      fn_name,
  ):
      dtype, x = dtype_and_x
      ret, gt_ret = helpers.test_function(
          input_dtypes=dtype,
          test_flags=test_flags,
          test_values=False,
          backend_to_test=backend_fw,
          on_device=on_device,
          fn_name=fn_name,
          x=x[0],
          prob=prob,
          training=training,
          data_format=data_format,
          return_flat_np_arrays=True,
      )
      ret = helpers.flatten_and_to_np(backend=backend_fw, ret=ret)
      gt_ret = helpers.flatten_and_to_np(
          backend=test_flags.ground_truth_backend, ret=gt_ret
      )
      for u, v, w in zip(ret, gt_ret, x):
          # cardinality test
          assert u.shape == v.shape == w.shape
  
imports: import numpy
  import torch
  from hypothesis import strategies, assume
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, BackendHandler
label_desc: Use image node_id 22 for calling a global function with example usage: test_dropout1d() without return types