quant_dim = mlir::cast<ShapedType>(res.getType()).getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

func.func @fake_quant_with_min_max_vars(%arg0: tensor<1x1x28x48xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<1x1x28x48xf32> {
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {device = "", narrow_range = true, num_bits = 8 : i64} : (tensor<1x1x28x48xf32>, tensor<f32>, tensor<f32>) -> tensor<1x1x28x48xf32>
  func.return %0 : tensor<1x1x28x48xf32>
  // CHECK-SKIP: tf.FakeQuantWithMinMaxVars
  // CHECK-NOSKIP-NOT: tf.

      quant_dim = mlir::cast<ShapedType>(res.getType()).getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

      quant_dim = input_type.getRank() - 1;
    }
    // Use the min/max from the operands and the num_bits and narrow_range
    // attribute to create the quantization parameter for the new quantize op.
    rewriter.setInsertionPointAfter(tf_op.getOperation());
    IntegerAttr num_bits = rewriter.getI64IntegerAttr(tf_op.getNumBits());
    BoolAttr narrow_range = rewriter.getBoolAttr(tf_op.getNarrowRange());

  let summary = [{
    Simulates the effect of uniform quantization with const range.
  }];

  let description = [{
    Given a const min, max, num_bits and narrow_range attribute, applies the
    same uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_args op. See the fakeQuantAttrsToType() utility

  let summary = [{
    Simulates the effect of uniform quantization with const range.
  }];

  let description = [{
    Given a const min, max, num_bits and narrow_range attribute, applies the
    same uniform quantization simulation as is done by the TensorFlow
    fake_quant_with_min_max_args op. See the fakeQuantAttrsToType() utility

				s.Label = labels
			}
			if len(numLabels) > 0 {
				s.NumLabel = numLabels
				for key, units := range numUnits {
					if len(units) > 0 {
						numUnits[key] = padStringArray(units, len(numLabels[key]))
					}
				}
				s.NumUnit = numUnits
			}
		}

		s.Location = locBuffer[:len(s.locationIDX)]
		locBuffer = locBuffer[len(s.locationIDX):]
		for i, lid := range s.locationIDX {

    """Quantizes and dequantizes hist_mids using quant_min and quant_max.

    Quantization converts the range of numbers from [quant_min, quant_max] to
    [0, 2^num_bits - 1]. Values less than quant_min are converted to 0, and
    values greater than quant_max are converted to 2^num_bits - 1.

    The histogram represents the distribution of the data, and our goal is to
    find the quant_min and quant_max that best describe this distribution. To do

  min = fakequant_op.getODSOperands(1);
  max = fakequant_op.getODSOperands(2);
  {
    auto target_attr = op->getAttrOfType<IntegerAttr>("num_bits");
    if (!target_attr)
      target_attr = rewriter.getIntegerAttr(rewriter.getIntegerType(64), 8);
    num_bits = target_attr;
  }
  {
    auto target_attr = op->getAttrOfType<BoolAttr>("narrow_range");

  // Release any underlying resources, including the interface object.
  virtual void Release() = 0;

  // Returns tensor dtype.
  virtual DataType Type() const = 0;
  // Returns number of dimensions.
  virtual int NumDims() const = 0;
  // Returns size of specified dimension
  virtual int64_t Dim(int dim_index) const = 0;
  // Returns number of elements across all dimensions.
  virtual int64_t NumElements() const = 0;