code: def test_sequential_layer(
      bs_c_target,
      with_v,
      seq_v,
      dtype,
      method_flags,
      on_device,
      backend_fw,
  ):
      with ivy.utils.backend.ContextManager(backend_fw):
          dtype = dtype[0]
          if backend_fw == "torch":
              assume("float16" not in dtype)
          if backend_fw == "paddle":
              assume(dtype != "float16")
          # smoke test
          batch_shape, channels, target = bs_c_target
          tolerance_dict = {
              "bfloat16": 1e-1,
              "float16": 1e-2,
              "float32": 1e-2,
              "float64": 1e-2,
          }
          if method_flags.as_variable[0]:
              x = _variable(
                  ivy.asarray(
                      ivy.linspace(
                          ivy.zeros(batch_shape),
                          ivy.ones(batch_shape),
                          channels,
                      ),
                      dtype=dtype,
                  )
              )
          else:
              x = ivy.asarray(
                  ivy.linspace(
                      ivy.zeros(batch_shape),
                      ivy.ones(batch_shape),
                      channels,
                  ),
                  dtype=dtype,
              )
          if with_v:
              np.random.seed(0)
              wlim = (6 / (channels + channels)) ** 0.5
              v = Container(
                  {
                      "submodules": {
                          "v0": {
                              "w": _variable(
                                  ivy.array(
                                      np.random.uniform(
                                          -wlim,
                                          wlim,
                                          (channels, channels),
                                      ),
                                      dtype=dtype,
                                      device=on_device,
                                  )
                              ),
                              "b": _variable(
                                  ivy.zeros(
                                      [channels],
                                      device=on_device,
                                      dtype=dtype,
                                  )
                              ),
                          },
                          "v2": {
                              "w": _variable(
                                  ivy.array(
                                      np.random.uniform(
                                          -wlim,
                                          wlim,
                                          (channels, channels),
                                      ),
                                      dtype=dtype,
                                      device=on_device,
                                  )
                              ),
                              "b": _variable(
                                  ivy.zeros(
                                      [channels],
                                      device=on_device,
                                      dtype=dtype,
                                  )
                              ),
                          },
                      }
                  }
              )
          else:
              v = None
          if seq_v:
              seq = ivy.Sequential(
                  ivy.Linear(
                      channels, channels, device=on_device, dtype=dtype
                  ),
                  ivy.Dropout(0.0, dtype=dtype),
                  ivy.Linear(
                      channels, channels, device=on_device, dtype=dtype
                  ),
                  device=on_device,
                  v=v if with_v else None,
                  dtype=dtype,
              )
          else:
              seq = ivy.Sequential(
                  ivy.Linear(
                      channels,
                      channels,
                      device=on_device,
                      v=v["submodules"]["v0"] if with_v else None,
                      dtype=dtype,
                  ),
                  ivy.Dropout(0.0, dtype=dtype),
                  ivy.Linear(
                      channels,
                      channels,
                      device=on_device,
                      v=v["submodules"]["v2"] if with_v else None,
                      dtype=dtype,
                  ),
                  device=on_device,
                  dtype=dtype,
              )
          ret = seq(x)
          # type test
          assert ivy.is_ivy_array(ret)
          # cardinality test
          assert ret.shape == ivy.Shape(batch_shape + [channels])
          # value test
          if not with_v:
              return
          assert np.allclose(
              ivy.to_numpy(seq(x)),
              np.array(target),
              rtol=tolerance_dict[dtype],
          )
  
imports: import numpy
  from hypothesis import assume
  from hypothesis import strategies
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy.data_classes.container import Container
  from ivy.functional.ivy.gradients import _variable
  from ivy.functional.ivy.layers import _deconv_length
  from ivy_tests.test_ivy.helpers import handle_method
  from ivy_tests.test_ivy.helpers.assertions import assert_same_type_and_shape
  from ivy_tests.test_ivy.test_functional.test_experimental.test_nn import test_layers
  from ivy_tests.test_ivy.test_functional.test_experimental.test_nn.test_layers import _valid_dct
label_desc: Use image node_id 33 for calling a global function with example usage: test_sequential_layer(bs_c_target, with_v, seq_v, dtype, method_flags, on_device, backend_fw) without return types