code: def _flatten_data_helper(draw):
      mixed_fn_compos = draw(st.booleans())
      is_torch_backend = ivy.current_backend_str() == "torch"
  
      dtype_and_x = draw(
          helpers.dtype_and_values(
              available_dtypes=helpers.get_dtypes(
                  "valid", mixed_fn_compos=mixed_fn_compos
              ),
              shape=st.shared(helpers.get_shape(), key="flatten_shape"),
          )
      )
      axes = draw(
          helpers.get_axis(
              shape=st.shared(helpers.get_shape(), key="flatten_shape"),
              min_size=2,
              max_size=2,
              unique=False,
              force_tuple=True,
          )
      )
      order = draw(st.sampled_from(["C", "F"]))
      if not mixed_fn_compos and is_torch_backend:
          order = "C"
      return dtype_and_x, axes, order
  
imports: from hypothesis import strategies, assume
  import hypothesis.extra.numpy
  import numpy
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, create_concatenable_arrays_dtypes
  from ivy.functional.ivy.experimental.manipulation import _check_bounds
  from ivy_tests.test_ivy.test_functional.test_core.test_manipulation import _get_splits
label_desc: Use image node_id 4 for calling a global function with example usage: _flatten_data_helper(draw) and returns: dtype_and_x,  axes,  order