code: def numba_funcify_ScalarOp(op, node, **kwargs):
      # TODO: Do we need to cache these functions so that we don't end up
      # compiling the same Numba function over and over again?
  
      if not hasattr(op, "nfunc_spec"):
          return generate_fallback_impl(op, node, **kwargs)
  
      scalar_func_path = op.nfunc_spec[0]
      scalar_func_numba = None
  
      *module_path, scalar_func_name = scalar_func_path.split(".")
      if not module_path:
          # Assume it is numpy, and numba has an implementation
          scalar_func_numba = getattr(np, scalar_func_name)
  
      input_dtypes = [
          np.dtype(input.type.dtype) for input in node.inputs
      ]
      output_dtypes = [
          np.dtype(output.type.dtype) for output in node.outputs
      ]
  
      if len(output_dtypes) != 1:
          raise ValueError(
              "ScalarOps with more than one output are not supported"
          )
  
      output_dtype = output_dtypes[0]
  
      input_inner_dtypes = None
      output_inner_dtype = None
  
      # Cython functions might have an additional argument
      has_pyx_skip_dispatch = False
  
      if scalar_func_path.startswith("scipy.special"):
          import scipy.special.cython_special
  
          cython_func = getattr(
              scipy.special.cython_special, scalar_func_name, None
          )
          if cython_func is not None:
              # try:
              scalar_func_numba = wrap_cython_function(
                  cython_func, output_dtype, input_dtypes
              )
              has_pyx_skip_dispatch = (
                  scalar_func_numba.has_pyx_skip_dispatch
              )
              input_inner_dtypes = scalar_func_numba.numpy_arg_dtypes()
              output_inner_dtype = (
                  scalar_func_numba.numpy_output_dtype()
              )
              # except NotImplementedError:
              #    pass
  
      if scalar_func_numba is None:
          scalar_func_numba = generate_fallback_impl(op, node, **kwargs)
  
      scalar_op_fn_name = get_name_for_object(scalar_func_numba)
      unique_names = unique_name_generator(
          [scalar_op_fn_name, "scalar_func_numba"], suffix_sep="_"
      )
  
      global_env = {"scalar_func_numba": scalar_func_numba}
  
      if input_inner_dtypes is None and output_inner_dtype is None:
          unique_names = unique_name_generator(
              [scalar_op_fn_name, "scalar_func_numba"], suffix_sep="_"
          )
          input_names = ", ".join(
              [unique_names(v, force_unique=True) for v in node.inputs]
          )
          if not has_pyx_skip_dispatch:
              scalar_op_src = f"""
  def {scalar_op_fn_name}({input_names}):
      return scalar_func_numba({input_names})
              """
          else:
              scalar_op_src = f"""
  def {scalar_op_fn_name}({input_names}):
      return scalar_func_numba({input_names}, np.intc(1))
              """
  
      else:
          global_env["direct_cast"] = numba_basic.direct_cast
          global_env["output_dtype"] = np.dtype(output_inner_dtype)
          input_tmp_dtype_names = {
              f"inp_tmp_dtype_{i}": i_dtype
              for i, i_dtype in enumerate(input_inner_dtypes)
          }
          global_env.update(input_tmp_dtype_names)
  
          unique_names = unique_name_generator(
              [scalar_op_fn_name, "scalar_func_numba"]
              + list(global_env.keys()),
              suffix_sep="_",
          )
  
          input_names = [
              unique_names(v, force_unique=True) for v in node.inputs
          ]
          converted_call_args = ", ".join(
              [
                  f"direct_cast({i_name}, {i_tmp_dtype_name})"
                  for i_name, i_tmp_dtype_name in zip(
                      input_names, input_tmp_dtype_names.keys()
                  )
              ]
          )
          if not has_pyx_skip_dispatch:
              scalar_op_src = f"""
  def {scalar_op_fn_name}({', '.join(input_names)}):
      return direct_cast(scalar_func_numba({converted_call_args}), output_dtype)
              """
          else:
              scalar_op_src = f"""
  def {scalar_op_fn_name}({', '.join(input_names)}):
      return direct_cast(scalar_func_numba({converted_call_args}, np.intc(1)), output_dtype)
              """
  
      scalar_op_fn = compile_function_src(
          scalar_op_src, scalar_op_fn_name, {**globals(), **global_env}
      )
  
      signature = create_numba_signature(node, force_scalar=True)
  
      return numba_basic.numba_njit(
          signature,
          fastmath=config.numba__fastmath,
          # Functions that call a function pointer can't be cached
          cache=False,
      )(scalar_op_fn)
  
imports: import math
  import numpy
  from pytensor import config
  from pytensor.compile.ops import ViewOp
  from pytensor.graph.basic import Variable
  from pytensor.link.numba.dispatch import basic
  from pytensor.link.numba.dispatch.basic import create_numba_signature, generate_fallback_impl, numba_funcify
  from pytensor.link.numba.dispatch.cython_support import wrap_cython_function
  from pytensor.link.utils import compile_function_src, get_name_for_object, unique_name_generator
  from pytensor.scalar.basic import Add, Cast, Clip, Composite, Identity, Mul, Reciprocal, ScalarOp, Second, Switch
  from pytensor.scalar.math import Erf, Erfc, GammaLn, Log1mexp, Sigmoid
label_desc: Use image node_id 1 for calling a global function with example usage: numba_funcify_ScalarOp(op, node) and returns: numba_basic, generate_fallback_impl