code: def get_mean_cov_vector(draw):
      input_dtype = draw(
          st.shared(
              st.sampled_from(draw(helpers.get_dtypes("float"))),
              key="shared_dtype",
          )
      )
      shared_size = draw(
          st.shared(
              helpers.ints(min_value=2, max_value=4), key="shared_size"
          )
      )
  
      # Generate shape for mean vector (..., n)
      dtype_mean = draw(
          helpers.array_values(
              dtype=input_dtype,
              shape=(shared_size,),
              min_value=2,
              max_value=5,
          )
      )
  
      # Generate shape for covariance matrix (..., n, n)
      dtype_cov = draw(
          helpers.array_values(
              dtype=input_dtype,
              shape=(shared_size, shared_size),
              min_value=2,
              max_value=5,
          ).filter(
              lambda x: np.linalg.cond(x.tolist())
              < 1 / sys.float_info.epsilon
          )
      )
  
      batch_shape = dtype_cov.shape[:-2]
  
      return input_dtype, dtype_mean, dtype_cov, batch_shape
  
imports: import pytest
  from hypothesis import strategies
  import ivy
  import numpy
  import sys
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
label_desc: Use image node_id 4 for calling a global function with example usage: get_mean_cov_vector(draw) and returns: input_dtype,  dtype_mean,  dtype_cov,  batch_shape