code: def array_and_broadcastable_shape(draw, dtype):
      """Return an array and a shape that the array can be broadcast to."""
      if isinstance(dtype, st._internal.SearchStrategy):
          dtype = draw(dtype)
          dtype = dtype[0] if isinstance(dtype, list) else draw(dtype)
      in_shape = draw(nph.array_shapes(min_dims=1, max_dims=4))
      x = draw(array_values(shape=in_shape, dtype=dtype))
      to_shape = draw(
          mutually_broadcastable_shapes(1, base_shape=in_shape)
          .map(lambda S: S[0])
          .filter(lambda s: broadcast_shapes(in_shape, s) == s),
          label="shape",
      )
      return x, to_shape
  
imports: import numpy
  import hypothesis.extra.numpy
  from hypothesis import strategies
  from hypothesis.internal.floats import float_of
  from functools import reduce
  from operator import mul
  import sys
  import string
  import ml_dtypes
  import ivy_tests.test_ivy.helpers.globals
  from ..pipeline_helper import WithBackendContext
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers.hypothesis_helpers.dtype_helpers import get_dtypes
  from .None import general_helpers
  from .None import dtype_helpers, number_helpers
  from ..globals import mod_backend
label_desc: Use image node_id 16 for calling a global function with example usage: array_and_broadcastable_shape(draw, dtype) and returns: x,  to_shape