func.func @quantized_tensor(%arg0: tensor<1x10x!quant.uniform<i8:f32, 0.0038396485615521669:-128>>) -> tensor<1x10x!quant.uniform<i8:f32, 3.906250e-03:-128>> {
  %0 = "tf.Intermediate"(%arg0) : (tensor<1x10x!quant.uniform<i8:f32, 0.0038396485615521669:-128>>) -> tensor<1x10x!quant.uniform<i8:f32, 3.906250e-03:-128>>
  func.return %0 : tensor<1x10x!quant.uniform<i8:f32, 3.906250e-03:-128>>

  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"

    %cst_0 = arith.constant dense<1> : tensor<256x256xi8>
    %cst_1 = "tfl.pseudo_qconst"() {qtype = tensor<256x256x!quant.uniform<i8:f32, 1.000000e+00>>, value = dense<1>  : tensor<256x256xi8>} : () -> tensor<256x256x!quant.uniform<i8:f32, 1.000000e+00>>
    %cst_2 = arith.constant dense<0> : tensor<i32>

      %2 = "quantfork.dcast"(%1) : (tensor<1x3x!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<1x3xf32>
      %3 = stablehlo.reshape %2 : (tensor<1x3xf32>) -> tensor<3x1xf32>
      %4 = "quantfork.qcast"(%3) {volatile} : (tensor<3x1xf32>) -> tensor<3x1x!quant.uniform<i8:f32, 0.13170163023705575:-1>>

#include "mlir/Support/LLVM.h"  // from @llvm-project

namespace mlir {
namespace quant {

// Creates a `UniformQuantizedType` with the given `scale` and `zero_point`
// values. The produced type has f32 as its expressed type and i8 as its
// storage type. The available values use the full range of the storage value,
// i.e. [-128, 127]. Assumes asymmetric quantization, meaning the zero point
// value can be a non-zero value.

func _[
	Int8 ~int8,
	Int16 ~int16,
	Int32 ~int32,
	Int64 ~int64,
	Int8_16 ~int8 | ~int16,
](
	i8 Int8,
	i16 Int16,
	i32 Int32,
	i64 Int64,
	i8_16 Int8_16,
) {
	b = 42
	b = 42.0
	// etc.

	i8 = -1 << 7
	i8 = 1<<7 - 1
	i16 = -1 << 15
	i16 = 1<<15 - 1
	i32 = -1 << 31
	i32 = 1<<31 - 1
	i64 = -1 << 63
	i64 = 1<<63 - 1

// operate on quantized values.
//
// Examples from storage to quantized type:
//   i8 -> !quant<"uniform[i8:f32]{1.0}">
//   tensor<4xi8> -> tensor<4x!quant<"uniform[i8:f32]{1.0}">>
//   vector<4xi8> -> vector<4x!quant<"uniform[i8:f32]{1.0}">>
def Quantization_StorageCastOp : Quantization_Op<"scast", [Pure]> {
  let arguments = (ins quant_RealOrStorageValueType:$arg);

  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"

  attr {
    key: "Tshape"
    value {
      type: DT_INT32
    }
  }
}

# MLIR-LABEL: func @main
# MLIR-SAME:  (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME:  control_outputs = ""
# MLIR-SAME:  inputs = "input"
# MLIR-SAME:  outputs = "output"

  %0 = "quantfork.qcast"(%cst_1) : (tensor<2x3x3x2xf32>) -> tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32:3, {0.003937007874015748,0.003937007874015748}>>
  %1 = "quantfork.dcast"(%0) : (tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32:3, {0.003937007874015748,0.003937007874015748}>>) -> tensor<2x3x3x2xf32>
  %2 = "quantfork.qcast"(%arg0) : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0011764706057660721:-43>>