code: def requantize_config(**kwargs):
      """Configure the requantization behavior by setting config variables.
  
      Parameters
      ---------
      rounding: "UPWARD" or "TONEAREST"
          Rounding direction for fixed point multiplications.
      compute_dtype:
          Specifies the data type used during requantize.
          Supported options: \"int64\", \"float32\", \"float64\"
  
      Returns
      -------
      config: RequantizeConfig
          The requantization configuration
      """
      node_args = {
          k: v() if k not in kwargs else kwargs[k]
          for k, v in RequantizeConfig._node_defaults.items()
      }
      return tvm.ir.make_node(
          "relay.qnn.op.RequantizeConfig", **node_args
      )
  
imports: from __future__ import absolute_import
  import tvm
  import tvm.ir
  from tvm import relay
  from tvm.relay.expr import Tuple, TupleWrapper
  from tvm.relay.op.nn.utils import get_pad_tuple2d
  from tvm.runtime import Object
  from tvm.target import Target
  from tvm.topi.nn.qnn import SQNN_DTYPE_TO_CODE
  from tvm.target.x86 import target_has_features
  from .None import _make, _requantize
label_desc: Use image node_id 2 for calling a global function with example usage: requantize_config() and returns: tvm