auto tranpose_op = *it;
    for (auto tranpose_operand : tranpose_op.getOperands()) {
      auto ranked_tranpose_type =
          mlir::dyn_cast_or_null<RankedTensorType>(tranpose_operand.getType());
      if (!ranked_tranpose_type) continue;
      if (ranked_tranpose_type.getRank() == permutation.size() &&
          operand->get().getType() ==

  if (!attr_type) return attr;

  if (attr_type.getValue() == "tensor") {
    if (auto f = mlir::dyn_cast<FloatAttr>(attr)) {
      RankedTensorType type = RankedTensorType::get({}, f.getType());
      return DenseFPElementsAttr::get(type, attr);
    }
    // TODO(fengliuai): handles ArrayAttr. Note that it can be nested ArrayAttr.
  }

  return attr;
}

          }
          Attribute value;
          if (isDenseI64Array(op_name, key)) {
            value = builder->getDenseI64ArrayAttr(vec);
          } else {
            RankedTensorType ty = tensorflow::GetTypeFromTFTensorShape(
                shape, builder->getIntegerType(64));
            value = DenseIntElementsAttr::get(ty, vec);
          }

    // Populate argument shapes.
    *arg->mutable_shape() = tensorflow::TensorShapeProto();
    if (auto ranked_tensor_type =
            mlir::dyn_cast<RankedTensorType>(operand_type)) {
      tensorflow::TensorShapeProto shape_proto;
      ConvertToTensorShapeProto(ranked_tensor_type.getShape(), &shape_proto);
      *arg->mutable_shape() = std::move(shape_proto);
    } else {

    int64_t multiplier = weight_shape[3] / in_channels;

    TensorType new_shape = RankedTensorType::get(
        {weight_shape[0], weight_shape[1], in_channels, multiplier},
        weight_type.getElementType());

    int cur_rank = weight_type.getRank();

    // Inserts a reshape op.
    auto shape_spec_type =
        RankedTensorType::get({cur_rank}, rewriter.getIntegerType(64));
    auto new_shape_const_attr =

  // TODO - b/278949920: Enable Per-Channel Quantization for XLA Opset
  auto qtype = mlir::dyn_cast<UniformQuantizedType>(quant_type);
  TensorType scale_type = RankedTensorType::get({}, rewriter.getF32Type());
  Value scale_op = rewriter.create<TF::ConstOp>(
      loc, scale_type,
      DenseFPElementsAttr::get(scale_type,
                               {static_cast<float>(qtype.getScale())}));

        min_max_values[1] == min_max_values[3]) {
      return failure();
    }

    mlir::ElementsAttr layer_stats = mlir::DenseFPElementsAttr::get(
        mlir::RankedTensorType::get({2}, rewriter.getF32Type()),
        {llvm::minimum(min_max_values[0], min_max_values[2]),
         llvm::maximum(min_max_values[1], min_max_values[3])});
    mlir::ElementsAttr axis_stats;

      // https://www.tensorflow.org/api_docs/python/tf/raw_ops/TensorArrayWriteV3
      Value flow = tensor_array_v3_op.getFlow();
      Type scalar_f32_tensor_type =
          RankedTensorType::get(/*shape=*/{}, rewriter.getF32Type());
      flow.setType(scalar_f32_tensor_type);
    }

    // Sets operand_segment_sizes or result_segment_sizes attribute to the op.

  void runOnOperation() final;
};

// Returns the type of the local variable for the stack size.
Type GetSizeVarType(OpBuilder builder) {
  auto size_type = cutil::GetSizeType(builder);
  return RankedTensorType::get(
      {}, TF::ResourceType::get(ArrayRef<TensorType>{size_type},
                                builder.getContext()));
}

// Returns the aliasing argument number of a fucntion return value if it simply

}

// Converts the condition for an IfOp/WhileOp to a boolean value.
Value ConvertConditionToBoolean(Operation* op, Value cond) {
  if (auto ranked_type = mlir::dyn_cast<RankedTensorType>(cond.getType()))
    if (ranked_type.getRank() == 0 &&
        ranked_type.getElementType().isSignlessInteger(1))
      return cond;

  OpBuilder builder(op);