code: def dequantize(
      data, input_scale, input_zero_point, axis=-1, out_dtype="float32"
  ):
      r"""Dequantize op
      This operator takes quantized input and produces dequantized float output.
      The output shape is the same as input shape. The input tensor can be of any shape.
  
      Parameters
      ----------
      data : tvm.relay.Expr
          The input tensor to be dequantized. Can be of type [int8, unit8, int16, uint16, int32].
  
      input_scale : tvm.relay.Expr
          The input scale.
  
      input_zero_point : tvm.relay.Expr
          The input 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 [float16, float32].
  
      Returns
      -------
      result : tvm.relay.Expr
          The computed result.
      """
  
      return _make.dequantize(
          data, input_scale, input_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 6 for calling a global function with example usage: dequantize(data, input_scale, input_zero_point, axis, out_dtype) and returns: _make