code: def _x_and_mha(draw):
      dtype = draw(
          helpers.get_dtypes("float", full=False).filter(
              lambda x: x != ["float16"]
          )
      )
      with_to_q_fn = draw(st.booleans())
      with_to_kv_fn = draw(st.booleans())
      with_to_out_fn = draw(st.booleans())
      query_dim = draw(st.integers(min_value=1, max_value=3))
      num_heads = draw(st.integers(min_value=1, max_value=3))
      head_dim = draw(st.integers(min_value=1, max_value=3))
      dropout_rate = draw(st.floats(min_value=0.0, max_value=0.9))
      context_dim = draw(st.integers(min_value=1, max_value=3))
      scale = draw(st.integers(min_value=1, max_value=3))
  
      num_queries = draw(st.integers(min_value=1, max_value=3))
      # x_feats = draw(st.integers(min_value=1, max_value=3))
      # cont_feats = draw(st.integers(min_value=1, max_value=3))
      num_keys = draw(st.integers(min_value=1, max_value=3))
      if with_to_q_fn:
          inputs_shape = (num_queries, query_dim)
      else:
          inputs_shape = (num_queries, num_heads * head_dim)
      if with_to_kv_fn:
          context_shape = (num_keys, context_dim)
      else:
          context_shape = (num_keys, num_heads * head_dim * 2)
      mask_shape = (num_queries, num_keys)
      x_mha = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=inputs_shape,
              min_value=0.0999755859375,
              max_value=1,
          )
      )
      context = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=context_shape,
              min_value=0.0999755859375,
              max_value=1,
          )
      )
      mask = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=mask_shape,
              min_value=0.0999755859375,
              max_value=1,
          )
      )
      return (
          dtype,
          x_mha,
          scale,
          num_heads,
          context,
          mask,
          query_dim,
          head_dim,
          dropout_rate,
          context_dim,
          with_to_q_fn,
          with_to_kv_fn,
          with_to_out_fn,
      )
  
imports: import numpy
  from hypothesis import assume
  from hypothesis import strategies
  import ivy
  import ivy_tests.test_ivy.helpers
  from ivy.data_classes.container import Container
  from ivy.functional.ivy.gradients import _variable
  from ivy.functional.ivy.layers import _deconv_length
  from ivy_tests.test_ivy.helpers import handle_method
  from ivy_tests.test_ivy.helpers.assertions import assert_same_type_and_shape
  from ivy_tests.test_ivy.test_functional.test_experimental.test_nn import test_layers
  from ivy_tests.test_ivy.test_functional.test_experimental.test_nn.test_layers import _valid_dct
label_desc: Use image node_id 6 for calling a global function with example usage: _x_and_mha(draw) and returns: dtype,  x_mha,  scale,  num_heads,  context,  mask,  query_dim,  head_dim,  dropout_rate,  context_dim,  with_to_q_fn,  with_to_kv_fn,  with_to_out_fn