code: def _get_dtype_and_matrix(
      draw, dtype="valid", square=False, invertible=False, batch=False
  ):
      if batch:
          arbitrary_dims = draw(helpers.get_shape(max_dim_size=3))
      else:
          arbitrary_dims = []
      if square:
          random_size = draw(st.integers(1, 5))
          shape = (*arbitrary_dims, random_size, random_size)
      else:
          shape = (
              *arbitrary_dims,
              draw(st.integers(1, 5)),
              draw(st.integers(1, 5)),
          )
      ret = helpers.dtype_and_values(
          available_dtypes=helpers.get_dtypes(dtype, full=True),
          min_value=-10,
          max_value=10,
          abs_smallest_val=1e-04,
          shape=shape,
      )
      if invertible:
          ret = ret.filter(
              lambda x: np.all(
                  np.linalg.cond(x[1]) < 1 / sys.float_info.epsilon
              )
          )
      return draw(ret)
  
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 3 for calling a global function with example usage: _get_dtype_and_matrix(draw, dtype, square, invertible, batch) and returns: draw