Args:
          weight_shape: Shape of the weight tensor.
          same_scale_op: Name of the same-scale op to be tested. Raises error
            when an unknown name is given.
        """
        self.filters = np.random.uniform(low=-1.0, high=1.0, size=weight_shape)
        self.same_scale_op = same_scale_op

      @def_function.function
      def matmul_and_same_scale(
          self, input_tensor: core.Tensor

      self,
      same_scale_op: str,
  ):
    input_shape = (2, 3, 1, 1024)
    filter_shape = (2, 3, 1024, 3)
    static_input_shape = [dim if dim is not None else 2 for dim in input_shape]

    model = self._create_matmul_and_same_scale_model(
        input_shape,
        filter_shape,
        self._input_saved_model_path,
        same_scale_op,
    )

  // routines need to be added around that op. When the assumption changes,
  // this policy might change as well.
  bool IsConnectedWithCompsiteFunction(Operation* same_scale_op) const {
    for (const auto& operand : same_scale_op->getOperands()) {
      auto dq_op = dyn_cast_or_null<quantfork::DequantizeCastOp>(
          operand.getDefiningOp());
      if (!dq_op) continue;

        has_quantized_types = true;
      }
    }
  }
  return has_quantized_types;
}

bool IsConnectedWithQuantizedCompsiteFunction(Operation* same_scale_op) {
  for (const Value operand : same_scale_op->getOperands()) {
    auto dq_op =
        dyn_cast_or_null<quantfork::DequantizeCastOp>(operand.getDefiningOp());
    if (!dq_op) continue;

// could not beat the overhead of the quantize and dequantize routines need to
// be added around that op. When the assumption changes, this policy might
// change as well.
bool IsConnectedWithQuantizedCompsiteFunction(Operation* same_scale_op);

// A base rewrite pattern which matches any N-in-M-out operations with
// quantization parameters propagated to at least one of its operands. The

    stats_op.erase();
  }

  // Returns false if the steps finish without errors.
  return false;
}

LogicalResult VerifySameScales(Operation* op) {
  auto same_scale_op = cast<SameScalesOpInterface>(op);

  SmallVector<QuantizedType, 4> collected_quant_params;
  for (Value input : op->getOperands()) {
    QuantizedType quant_params =