code: def _x_and_scaled_attention(draw, dtypes):
      dtype = draw(dtypes)
      num_queries = draw(helpers.ints(min_value=2, max_value=4))
      num_keys = draw(helpers.ints(min_value=2, max_value=4))
      feat_dim = draw(helpers.ints(min_value=2, max_value=4))
      batch_size = draw(helpers.ints(min_value=1, max_value=2))
      q_shape = (batch_size,) + (num_queries,) + (feat_dim,)
      k_shape = (batch_size,) + (num_keys,) + (feat_dim,)
      v_shape = (batch_size,) + (num_keys,) + (feat_dim,)
      mask_shape = (batch_size,) + (num_queries,) + (num_keys,)
  
      query = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=q_shape,
              min_value=0,
              max_value=1e2,
              large_abs_safety_factor=7,
              small_abs_safety_factor=7,
              safety_factor_scale="linear",
          )
      )
      key = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=k_shape,
              min_value=0,
              max_value=1e2,
              large_abs_safety_factor=7,
              small_abs_safety_factor=7,
              safety_factor_scale="linear",
          )
      )
      value = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=v_shape,
              min_value=0,
              max_value=1e2,
              large_abs_safety_factor=7,
              small_abs_safety_factor=7,
              safety_factor_scale="linear",
          )
      )
      mask = draw(
          helpers.array_values(
              dtype="bool",
              shape=mask_shape,
          )
          | st.none()
      )
      return dtype, query, key, value, mask
  
imports: import ivy
  from hypothesis import assume, strategies
  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: _x_and_scaled_attention(draw, dtypes) and returns: dtype,  query,  key,  value,  mask