code: def _get_second_matrix_and_dtype(draw, *, transpose=False):
      # batch_shape, shared, random_size
      input_dtype = draw(
          st.shared(
              st.sampled_from(draw(helpers.get_dtypes("numeric"))),
              key="shared_dtype",
          ).filter(lambda x: "float16" not in x)
      )
      shared_size = draw(
          st.shared(
              helpers.ints(min_value=2, max_value=4), key="shared_size"
          )
      )
      random_size = draw(
          st.shared(
              helpers.ints(min_value=2, max_value=4), key="shared_size"
          )
      )
      matrix = draw(
          helpers.array_values(
              dtype=input_dtype,
              shape=(random_size, shared_size),
              min_value=2,
              max_value=5,
          )
      )
      if transpose:
          transpose = draw(st.booleans())
          adjoint = draw(st.booleans())
          if adjoint and transpose:
              adjoint = draw(st.just("False"))
          if transpose and not adjoint:
              matrix = np.transpose(matrix)
          if adjoint and not transpose:
              matrix = np.transpose(np.conjugate(matrix))
          return [input_dtype], matrix, transpose, adjoint
      return [input_dtype], matrix
  
imports: import sys
  import numpy
  from hypothesis import assume, strategies
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_test, BackendHandler
  from ivy_tests.test_ivy.helpers.hypothesis_helpers.general_helpers import matrix_is_stable
label_desc: Use image node_id 7 for calling a global function with example usage: _get_second_matrix_and_dtype(draw) and returns: list,  matrix, list,  matrix,  transpose,  adjoint