code: def _get_dtype_and_symmetrix_matrix(draw):
      input_dtype = draw(
          st.shared(st.sampled_from(draw(helpers.get_dtypes("valid"))))
      )
      random_size = draw(helpers.ints(min_value=2, max_value=4))
      batch_shape = draw(
          helpers.get_shape(min_num_dims=1, max_num_dims=3)
      )
      num_independnt_vals = int(
          (random_size**2) / 2 + random_size / 2
      )
      array_vals_flat = np.array(
          draw(
              helpers.array_values(
                  dtype=input_dtype,
                  shape=tuple(
                      list(batch_shape) + [num_independnt_vals]
                  ),
                  min_value=2,
                  max_value=5,
              )
          )
      )
      array_vals = np.zeros(batch_shape + (random_size, random_size))
      c = 0
      for i in range(random_size):
          for j in range(random_size):
              if j < i:
                  continue
              array_vals[..., i, j] = array_vals_flat[..., c]
              array_vals[..., j, i] = array_vals_flat[..., c]
              c += 1
      return [input_dtype], array_vals
  
imports: import math
  import sys
  import numpy
  from hypothesis import strategies, assume
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import assert_all_close
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_frontends.test_torch.test_miscellaneous_ops import dtype_value1_value2_axis
  from ivy_tests.test_ivy.helpers.hypothesis_helpers.general_helpers import matrix_is_stable
  from ivy_tests.test_ivy.test_functional.test_core.test_linalg import _matrix_rank_helper
label_desc: Use image node_id 4 for calling a global function with example usage: _get_dtype_and_symmetrix_matrix(draw) and returns: list,  array_vals