// CHECK-NEXT:      type: UINT8,
// CHECK-NEXT:      buffer: 2,
// CHECK-NEXT:      name: "arg1",
// CHECK-NEXT:      quantization: {
// CHECK-NEXT:        scale: [ 0.1 ],
// CHECK-NEXT:        zero_point: [ 0 ]
// CHECK-NEXT:      },
// CHECK-NEXT:      has_rank: true
// CHECK-NEXT:    }, {
// CHECK-NEXT:      shape: [ 4 ],
// CHECK-NEXT:      buffer: 3,
// CHECK-NEXT:      name: "NumericVerify/arg1:1",

    float max = 2;
  }

  // Affine parameters to quantize the per axis value. The metadata of the
  // target properties should be specified as well.
  message AffineParams {
    float scale = 1;
    int32 zero_point = 2;
  }

  // Params to quantize the axis. Only one of the field can be used.
  message PerAxisParams {
    oneof params_oneof {
      // min/max of the ranges.
      MinMax min_max = 1;

  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
  quantized_dimension:int;
}

// Sparse tensors.
// We use a modification of the TACO format.
// Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
//
// To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),

        flags, storage_type, builder.getF32Type(), scales,
        quant_params.zero_point, quant_params.quantized_dimension, storage_min,
        storage_max);
  }
  return mlir::quant::UniformQuantizedType::get(
      flags, storage_type, builder.getF32Type(), scales[0],
      quant_params.zero_point.at(0), storage_min, storage_max);
}

  // CHECK-NEXT:      type: UINT8,
  // CHECK-NEXT:      buffer: 3,
  // CHECK-NEXT:      name: "tfl.pseudo_qconst",
  // CHECK-NEXT:      quantization: {
  // CHECK-NEXT:        scale: [ 0.021827 ],
  // CHECK-NEXT:        zero_point: [ 151 ]
  // CHECK-NEXT:      },
  // CHECK-NEXT:      has_rank: true
  // CHECK-NEXT:    }, {
  // CHECK-NEXT:      shape: [ 32, 3, 3, 3 ],
  // CHECK-NEXT:      buffer: 4,

  //   t[:, 1, :, :] will have scale[1]=2.0, zero_point[0]=2
  //   t[:, 2, :, :] will have scale[2]=3.0, zero_point[0]=3
  quantized_dimension:int;
}

// Sparse tensors.
// We use a modification of the TACO format.
// Reference: http://tensor-compiler.org/kjolstad-oopsla17-tensor-compiler.pdf
//
// To encode a conceptual n-dimensional dense tensor with dims (d0, ..., dn-1),

//     1
//   - the specified min/max values are "nudged" so that the result has a zero
//     that can be exactly expressed
//   - min=max=0 implies scale=0 and zero_point=0
//
// With the above assumptions applied, every conforming specified FakeQuant op
// can be represented by a UniformQuantizedType. This scheme is not expected to

  // CHECK-NEXT:      type: UINT8,
  // CHECK-NEXT:      buffer: 3,
  // CHECK-NEXT:      name: "tfl.pseudo_qconst",
  // CHECK-NEXT:      quantization: {
  // CHECK-NEXT:        scale: [ 0.021827 ],
  // CHECK-NEXT:        zero_point: [ 151 ]
  // CHECK-NEXT:      },
  // CHECK-NEXT:      has_rank: true
  // CHECK-NEXT:    }, {
  // CHECK-NEXT:      shape: [ 32, 3, 3, 3 ],
  // CHECK-NEXT:      buffer: 4,

# RESULT1: name: "Identity"
# RESULT1-NEXT: quantization:
# RESULT1-NEXT: scale: [ 0.007523 ],
# RESULT1-NEXT: zero_point: [ 116 ]

# TODO  Actually RESULT1 represents in incomplete implementation
# Currently TF2.2.0-rc3 all but the first fake_quant in 

// CHECK-NEXT:       type: INT8,
// CHECK-NEXT:       name: "effective_hidden_scale_intermediate",
// CHECK-NEXT:       quantization: {
// CHECK-NEXT:         scale: [ 0.007788 ],
// CHECK-NEXT:         zero_point: [ 0 ]
// CHECK-NEXT:       },
// CHECK-NEXT:       has_rank: true
// CHECK-NEXT:     }, {
// CHECK-NEXT:       shape: [ 4, 4, 4 ],
// CHECK-NEXT:       buffer: 25,