code: def jax_typify_Generator(rng, **kwargs):
      state = rng.__getstate__()
      state["bit_generator"] = numpy_bit_gens[state["bit_generator"]]
  
      # XXX: Is this a reasonable approach?
      state["jax_state"] = _coerce_to_uint32_array(
          state["state"]["state"]
      )[0:2]
  
      # The "state" and "inc" values in a NumPy `Generator` are 128 bits, which
      # JAX can't handle, so we split these values into arrays of 32 bit integers
      # and then combine the first two into a single 64 bit integers.
      #
      # XXX: Depending on how we expect these values to be used, is this approach
      # reasonable?
      #
      # TODO: We might as well remove these altogether, since this conversion
      # should only occur once (e.g. when the graph is converted/JAX-compiled),
      # and, from then on, we use the custom "jax_state" value.
      inc_32 = _coerce_to_uint32_array(state["state"]["inc"])
      state_32 = _coerce_to_uint32_array(state["state"]["state"])
      state["state"]["inc"] = inc_32[0] << 32 | inc_32[1]
      state["state"]["state"] = state_32[0] << 32 | state_32[1]
      return state
  
imports: from functools import singledispatch
  import jax
  import numpy
  from numpy.random import Generator, RandomState
  from numpy.random.bit_generator import _coerce_to_uint32_array
  import pytensor.tensor.random.basic
  from pytensor.link.jax.dispatch.basic import jax_funcify, jax_typify
  from pytensor.link.jax.dispatch.shape import JAXShapeTuple
  from pytensor.tensor.shape import Shape, Shape_i
label_desc: Use image node_id 3 for calling a global function with example usage: jax_typify_Generator(rng) and returns: state