^bb0(%arg0: tensor<1x2xf32>):
  %cst_0 = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0){qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>}: (tensor<1x2xf32>) -> (tensor<1x2x!quant.uniform<u8:f32, 1.0>>)
  %1 = "tfl.dequantize"(%0): (tensor<1x2x!quant.uniform<u8:f32, 1.0>>) -> (tensor<1x2xf32>)
  %2 = "tf.Transpose"(%1, %cst_0): (tensor<1x2xf32>, tensor<2xi32>) -> tensor<2x1xf32>

  // The original model reshape->custom->custom->squeeze.
  ASSERT_THAT(*float_graph->operators(), SizeIs(4));
  // The resulting model should be:
  // reshape->dequantize->custom->custom->quantize->squeeze.
  ASSERT_THAT(subgraph->operators, SizeIs(6));
  const std::vector<BuiltinOperator> op_codes = {
      BuiltinOperator_RESHAPE,  BuiltinOperator_DEQUANTIZE,

  // before converting TF_Conv to TFL_Conv
  (void)applyPatternsAndFoldGreedily(func, std::move(patterns));

  // Remove the wrapper of the tf.FakeQuant* ops and also insert the
  // tfl.quantize and tfl.dequantize to preserve the quantization parameters.
  // This is done after the first round of optimization to make sure all the
  // min/max operands of the tf.FakeQuant* are constants to be matched. The

foreach BinaryOp = [TFL_DivOp, TFL_MulOp]<Op> in
  defm : FuseMulOrDivWithConv2dOrDepthwiseConv2d<BinaryOp>;


// This pattern applies when the same quantize/dequantize have been used twice
// with the same scale. We want to remove the redundancy.
// TODO(fengliuai): move this to the sanity check of pre-quantize pass.
def eliminate_dq_q_pairs : Pat<
  (TFL_QuantizeOp (TFL_DequantizeOp $in), $qt),
  (replaceWithValue $in),

      return emitError(loc, type_or_err.status().ToString()),
             type_or_err.status();
    }
    auto type = std::move(type_or_err).value();

    if (op_name == "tfl.quantize") {
      // Special case for quantize: return type must also be in qtype attribute
      op_state.addAttribute("qtype", mlir::TypeAttr::get(type));
    } else if (op_name == "tfl.reshape" && op_state.operands.size() == 1) {

        "transforms/post_quantize.cc",
        "transforms/prepare_quantize.cc",
        "transforms/prepare_quantize_dynamic_range.cc",
        "transforms/prepare_quantize_helper.cc",
        "transforms/quantize.cc",
        "transforms/quantize_variables.cc",
        "utils/generated_op_quant_spec_getters.inc",
    ],
    hdrs = [
        "transforms/passes.h",
        "transforms/prepare_quantize_helper.h",
    ],