code: def _partial_unfold_data(draw):
      dtype, input = draw(
          helpers.dtype_and_values(
              available_dtypes=helpers.get_dtypes("valid"),
              min_num_dims=1,
          )
      )
      ndims = len(input[0].shape)
      mode_and_skip_begin = draw(
          st.lists(
              helpers.ints(min_value=0, max_value=ndims - 1),
              min_size=2,
              max_size=2,
          ).filter(lambda nums: np.sum(nums) <= ndims - 1)
      )
      skip_begin, mode = sorted(mode_and_skip_begin)
      skip_end = draw(
          helpers.ints(
              min_value=0, max_value=ndims - (skip_begin + mode) - 1
          )
      )
      ravel_tensors = draw(st.booleans())
      return dtype, input, mode, skip_begin, skip_end, ravel_tensors
  
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 11 for calling a global function with example usage: _partial_unfold_data(draw) and returns: dtype,  input,  mode,  skip_begin,  skip_end,  ravel_tensors