code: def jax_funcify_CAReduce(op, **kwargs):
      axis = op.axis
      op_nfunc_spec = getattr(op, "nfunc_spec", None)
      scalar_nfunc_spec = getattr(op.scalar_op, "nfunc_spec", None)
      scalar_op_name = getattr(op.scalar_op, "name", None)
      scalar_op_identity = getattr(op.scalar_op, "identity", None)
      acc_dtype = getattr(op, "acc_dtype", None)
  
      def careduce(x):
          nonlocal axis, op_nfunc_spec, scalar_nfunc_spec, scalar_op_name, scalar_op_identity, acc_dtype
  
          if axis is None:
              axis = list(range(x.ndim))
  
          if acc_dtype is None:
              acc_dtype = x.dtype.type
  
          if op_nfunc_spec:
              jax_op = getattr(jnp, op_nfunc_spec[0])
              return jax_op(x, axis=axis).astype(acc_dtype)
  
          # The PyTensor `Op` didn't tell us which NumPy equivalent to use (or
          # there isn't one), so we use this fallback approach
          if scalar_nfunc_spec:
              scalar_fn_name = scalar_nfunc_spec[0]
          elif scalar_op_name:
              scalar_fn_name = scalar_op_name
  
          to_reduce = sorted(axis, reverse=True)
  
          if to_reduce:
              # In this case, we need to use the `jax.lax` function (if there
              # is one), and not the `jnp` version.
              jax_op = getattr(jax.lax, scalar_fn_name)
              init_value = jnp.array(
                  scalar_op_identity, dtype=acc_dtype
              )
              return jax.lax.reduce(
                  x, init_value, jax_op, to_reduce
              ).astype(acc_dtype)
          else:
              return x
  
      return careduce
  
file_path: pytensor/pytensor/link/jax/dispatch/elemwise.py
imports: import jax
  import jax.numpy
  from pytensor.link.jax.dispatch.basic import jax_funcify
  from pytensor.tensor.elemwise import CAReduce, DimShuffle, Elemwise
  from pytensor.tensor.special import LogSoftmax, Softmax, SoftmaxGrad
label_desc: Use image node_id 2 for calling a global function with example usage: jax_funcify_CAReduce(op) and returns: careduce, jax_op, jax, x