code: def quantize(
      data, output_scale, output_zero_point, axis=-1, out_dtype="int8"
  ):
      r"""Quantize op
      This operator takes float32 input and produces quantized output. The input
      tensor can be of any shape. The output shape is the same as input shape.
  
      Q_output = clamp((round(input_tensor/output_scale) + output_zero_point),
                       out_dtype::min,
                       out_dtype::max)
  
      Parameters
      ----------
      data : tvm.relay.Expr
          The input tensor to be quantized. Can be of type float32.
  
      output_scale : tvm.relay.Expr
          The output scale.
  
      output_zero_point : tvm.relay.Expr
          The output zero_point.
  
      axis : int
          The channel axis for quantization. Default value is -1 which corresponds to the last axis.
  
      out_dtype : str, optional
          The data type of the output tensor. Can be [int8, unit8, int16, uint16, int32].
  
      Returns
      -------
      result : tvm.relay.Expr
          The computed result.
      """
  
      return _make.quantize(
          data, output_scale, output_zero_point, axis, out_dtype
      )
  
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 4 for calling a global function with example usage: quantize(data, output_scale, output_zero_point, axis, out_dtype) and returns: _make