class_docstr: A `Linker` that JIT-compiles NumPy-based operations using Numba.
code: def create_thunk_inputs(self, storage_map):
      from numpy.random import RandomState
  
      from pytensor.link.numba.dispatch import numba_typify
  
      thunk_inputs = []
      for n in self.fgraph.inputs:
          sinput = storage_map[n]
          if isinstance(sinput[0], RandomState):
              new_value = numba_typify(
                  sinput[0], dtype=getattr(sinput[0], "dtype", None)
              )
              # We need to remove the reference-based connection to the
              # original `RandomState`/shared variable's storage, because
              # subsequent attempts to use the same shared variable within
              # other non-Numba-fied graphs will have problems.
              sinput = [new_value]
          thunk_inputs.append(sinput)
  
      return thunk_inputs
  
file_path: pytensor/pytensor/link/numba/linker.py
imports: from typing import TYPE_CHECKING, Any
  import numpy
  import pytensor
  from pytensor.link.basic import JITLinker
label_desc: Use image node_id 4 for calling the NumbaLinker obj's underlying member method code with example usage: obj.create_thunk_inputs(storage_map) and returns: thunk_inputs