code: def _quantize_mkldnn_min_max_int8(data, data_min, data_max):
      """Quantizes the given `data` in float32 and the given
      min and max ranges and the output data type is `int8`.
      The method of quantizing is described here - https://tinyurl.com/y5k6fz5w.
      We use our default quantize implementation from src/relay/qnn/op/quantize.cc:72
      but compute the `scale` and `zero_point` to fit our equation.
      Unlike in TFLite where we get the scale and zero_point from the model, MKLDNN
      stores the min and max from which we calculate the scale and zero_point.
  
      Parameters
      ----------
      data : tvm.relay.Expr
          The input tensor to be quantized. Can be of type float32.
      data_min : float
          The minimum to use data elements.
      data_max : float
          The maximum to use for data elements.
  
      Returns
      -------
      result : tvm.relay.Expr
          The computed result.
      """
  
      return _quantize_with_zero_centered(
          data,
          data_min,
          data_max,
          zero_centered_int8_quantized_range,
          "int8",
      )
  
file_path: tvm/python/tvm/relay/frontend/mxnet_qnn_op_utils.py
imports: import numpy
  from tvm import relay
  from tvm.relay.qnn.op.qnn import quantize, dequantize
label_desc: Use image node_id 5 for calling a global function with example usage: _quantize_mkldnn_min_max_int8(data, data_min, data_max) and returns: _quantize_with_zero_centered