%axis = "tf.Const"() {device = "", value = dense<1> : tensor<i32>} : () -> tensor<i32>
  // tf.ConcatV2 accepts a variadic operand. The last operand should be compile
  // time constant.
  %0 = "tf.ConcatV2"(%arg0, %arg0, %arg0, %arg0, %axis) : (tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<i32>) -> tensor<16x4xf32>

  // CHECK-SAME: (tensor<i32>, tensor<i32>, tensor<i32>) -> tensor<2xi32>
  %axis = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>
  %0 = "tf.VariableV2"() {container = "", shape = #tf_type.shape<>, shared_name = "x"} : () -> tensor<!tf_type.int32ref>
  %1 = "tf.ConcatV2"(%0, %0, %axis) : (tensor<!tf_type.int32ref>, tensor<!tf_type.int32ref>, tensor<i32>) -> tensor<2xi32>
  func.return %1 : tensor<2xi32>

    }
    // Make sure that the axis in `expand_op` is constant.
    if (auto const_op =
            llvm::dyn_cast<TF::ConstOp>(expand_op.getDim().getDefiningOp())) {
      expand_axis = (*mlir::cast<DenseElementsAttr>(const_op.getValue())
                          .getValues<APInt>()
                          .begin())
                        .getSExtValue();
      // Canonicalize axis. Some TF python functions, such as

             ">::Impl")>;

// Specify the operand index of the coefficient operand for an affine op
// and also the quantization dimension if per-axis quantization is support.
// If the quantization dimension is -1, per-axis quantization isn't supported.
class AffineOpCoefficient<int dim, int index> : NativeOpTrait<
  !strconcat("quant::AffineOpCoefficient<",
             !interleave([dim, index], ", "),

  %3 = "tfl.pack"(%1, %2) {tac.device = "CPU", tac.inference_type = "FLOAT", axis = 0 : i32, values_count = 2 : i32} : (tensor<1xf32>, tensor<1xf32>) -> tensor<2x1xf32>
  return %3 : tensor<2x1xf32>
}
```

In this code, `%3` is annotated with "CPU", while others are annotated with

    // new quantization dimension. Only if the new quantization dimension can
    // be inferred, it is safe to reset the per-axis quantized type.
    if (axis == -1) return {};
    qtype =
        ResetAxisAndBroadcast(source_type.getShape(), per_axis, target, axis);
  }
  if (!qtype) return {};
  const Type final_type = qtype.castFromExpressedType(target);
  if (!final_type) return {};

    Value operand = reduce_op.getInputs().front();
    int64_t axis = reduce_op.getDimensions().getValues<int64_t>()[0];

    auto dim_type = RankedTensorType::get({1}, rewriter.getI32Type());
    auto reduction_indices = rewriter.create<arith::ConstantOp>(
        reduce_op.getLoc(), dim_type,
        rewriter.getI32TensorAttr({static_cast<int32_t>(axis)}));

    // Generate a Max and an ArgMax of as the mhlo op returns both while in TF

// Eliminate cumulative summations if the input's dimension in axis is 1.
def EliminateCumSumInclusive : Pat<
  (TFL_CumsumOp
     $input,
     (Arith_ConstantOp I32ElementsAttr:$axis),
     ConstBoolAttrFalse,
     $reverse),
  (replaceWithValue $input),
  [(AreInputDimensionsOneInAxes $input, $axis)]>;

// Fusing raw computation of GELU op into one native tfl_gelu op.
//

      return emitOpError("layerStats must have shape [2]");
    }
  }
  // Verify axisStats (optional) attribute.
  if (getAxisStats()) {
    if (!getAxis()) return emitOpError("axis must be specified for axisStats");

    auto shape = tensorArg.getShape();
    auto argSliceSize =
        std::accumulate(std::next(shape.begin(), *getAxis()), shape.end(), 1,

      return emitOpError("layerStats must have shape [2]");
    }
  }
  // Verify axisStats (optional) attribute.
  if (getAxisStats()) {
    if (!getAxis()) return emitOpError("axis must be specified for axisStats");

    auto shape = tensorArg.getShape();
    auto argSliceSize =
        std::accumulate(std::next(shape.begin(), *getAxis()), shape.end(), 1,