code: def _shift(data, zero_point, out_dtype):
      """Shifts (add/subtracts) the qnn tensor with +/-128)"""
      if out_dtype == "uint8":
          shift = 128
      elif out_dtype == "int8":
          shift = -128
      else:
          raise ValueError("Unsupported out dtype.")
      data_modified = relay.cast(data, "int32")
      data_modified = relay.add(
          data_modified, relay.const(shift, "int32")
      )
      data_modified = relay.cast(data_modified, out_dtype)
      if isinstance(zero_point, relay.Constant):
          zero_point_val = get_scalar_from_constant(zero_point)
          zero_point_modified = relay.const(
              zero_point_val + shift, "int32"
          )
      else:
          zero_point_modified = zero_point + relay.const(shift, "int32")
      return (data_modified, zero_point_modified)
  
imports: import numpy
  from scipy import special
  import tvm
  from tvm import relay
  from tvm._ffi.base import TVMError
  from tvm.relay.qnn.op.canonicalizations import create_integer_lookup_op
  from ....target.x86 import target_has_features
  from ....topi.utils import is_target
  from ..None import op
label_desc: Use image node_id 10 for calling a global function with example usage: _shift(data, zero_point, out_dtype) and returns: data_modified,  zero_point_modified