SetWeightForRecurrentToForgetGate();
  SetWeightForRecurrentToOutputGate();

  // Extract bias to cifg gates via slicing the bias tensor
  SetBiasToCellGate();
  SetBiasToInputGate();
  SetBiasToForgetGate();
  SetBiasToOutputGate();

  // Extract projection and set an empty projection bias
  SetProjection();
  SetProjectionBias();

  // Set the variable tensors
  SetInputActivationState();

  // TensorFlow Conv3D has no bias, optimization patterns will fuse Conv3D
  // with other ops can fill the bias.
  Value none = rewriter.create<TFL::NoValueOp>(
      op->getLoc(), rewriter.getNoneType(), rewriter.getUnitAttr());

  rewriter.replaceOpWithNewOp<TFL::Conv3DOp>(
      op, tf_op.getType(), tf_op.getInput(), tf_op.getFilter(),
      /*bias=*/none, dilation_depth_factor, dilation_height_factor,

//                         PatternRewriter &rewriter, Location loc,
//                         Type result_type, Value input,
//                         Value filter, Value bias) const;
//
// And also the following method for getting the dimension for bias tensor:
//
//  int64_t getBiasDim(ArrayRef<int64_t> filterShape) const;
template <typename ConcreteType, typename TFConvOpType>
class ConvertTFConvOp : public RewritePattern {

  // tensor_proto that points to dense<127> of type !tf_type.qint32.
  // CHECK-DAG: %[[RHS:.*]] = mhlo.constant() <{value = dense<127> : tensor<2xi32>}> : () -> tensor<2x!quant.uniform<i32:f32, 2.000000e+00:4>>

def OptimizeIntGraph : Pass<"optimize-int-graph", "mlir::func::FuncOp"> {
  let summary = "Optimization patterns for quantized integer graph";

  let description = [{
    This includes patterns for merging addition of zp offset and bias.
  }];

  let constructor = "mlir::quant::stablehlo::CreateOptimizeIntGraphPass()";
  let dependentDialects = ["mhlo::MhloDialect"];

  %bias = arith.constant dense<1.0> : tensor<32xf32>
  %input = "tfl.dequantize"(%arg0) : (tensor<1x224x224x3x!quant.uniform<u8:f32, 1.5>>) -> tensor<1x224x224x3xf32>
  %weight = "tfl.dequantize"(%arg1) : (tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32:3, {1.0,2.0,3.0}>>) -> tensor<32x3x3x3xf32>
  %conv = "tfl.conv_2d"(%input, %weight, %bias) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32,

  // CHECK-DAG: %[[WEIGHTS:.*]] = arith.constant dense<1.000000e+00> : tensor<32x4x4x128xf32>
  // CHECK-DAG: %[[BIAS:.*]] = arith.constant dense<1.500000e+00> : tensor<32xf32>
  // CHECK: %[[RESULT:.*]] = "tfl.transpose_conv"(%[[SHAPE]], %[[WEIGHTS]], %arg0, %[[BIAS]])
  // CHECK: return %[[RESULT]]
}

// CHECK-LABEL: fuseMulIntoTransposeConv

			ebits = fbits - mbits - 1 //     8  exponent size
			bias  = 1<<(ebits-1) - 1  //   127  exponent bias
			dmin  = 1 - bias - mbits  //  -149  smallest unbiased exponent (denormal)
			emin  = 1 - bias          //  -126  smallest unbiased exponent (normal)
			emax  = bias              //   127  largest unbiased exponent (normal)
		)

    output = input / (bias + alpha * sqr_sum) ** beta

For details, see [Krizhevsky et al., ImageNet classification with deep
convolutional neural networks (NIPS 2012)](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks).
  }];

  let arguments = (ins
      TFL_FpTensor:$input,
      I32Attr:$radius,
      F32Attr:$bias,
      F32Attr:$alpha,

# MLIR:         %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0
# MLIR:         %[[weight:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x1x1x256x!quant.uniform<i8<-127:127>:f32:0, {0.12581039038230116,