if (!input_type || !input_type.hasStaticShape()) {
        return false;
      }
      total_count += input_type.getNumElements();
    }
    *count = total_count;
    return true;
  }

  // For conv2d/depthwise_conv/fully_connected ops.
  // This algorithm actually comes from TOCO tooling_util.cc
  static bool GetArithmeticCountForConvAndFullyconnectedOp(mlir::Operation* op,

                         %input : tensor<*xf32>, %weight : tensor<*x!tf_type.qint8>,
                         %weight_scale : tensor<*xf32>, %weight_zp : tensor<*xi32>) -> tensor<*xf32>
      attributes {tf_quant.quantized_ops = ["Conv2D"]} {

    %out = "tf.UniformQuantizedConvolutionHybrid"(%input, %weight,
                           %weight_scale, %weight_zp) {
        Tlhs = "tfdtype$DT_FLOAT",
        Trhs = "tfdtype$DT_QINT8",

// has a zero MTIME. This is a requirement for the Debian maintainers
// to be able to have deterministic packages.
//
// To patch a .gz file, use the following command:
//
//	$ dd if=/dev/zero bs=1 seek=4 count=4 conv=notrunc of=filename.gz
//
// See https://golang.org/issue/14937.
func TestGZIPFilesHaveZeroMTimes(t *testing.T) {
	// To avoid spurious false positives due to untracked GZIP files that

  %1 = "tf.FakeQuantWithMinMaxArgs"(%arg0) {device = "", max = 2.000000e-01 : f32, min = -1.000000e-01 : f32, narrow_range = false, num_bits = 8 : i64} : (tensor<1x3x4x3xf32>) -> tensor<*xf32>
  %2 = "tf.Conv2D"(%1, %0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<*xf32>, tensor<*xf32>) -> tensor<*xf32>

    %1 = "quantfork.stats"(%arg0) {layerStats = dense<[1.27501142, 149.824783]> : tensor<2xf32>} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3xf32>

// Checks if the value has only one user.
def HasOneUse : Constraint<CPred<"$0.hasOneUse()">>;

// If we see a Conv2D op followed by Mul, then multiply the filter
// with the value in Mul.
def FuseMulAndConv2D :
  Pat<(TF_MulOp:$mul (TF_Conv2DOp:$conv $input,
                          (Arith_ConstantOp:$filter F32ElementsAttr:$filter_value),

  %0 = "tf.Conv2D"(%output, %cst) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 2, 2, 1], use_cudnn_on_gpu = true}> {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", device...

    %3 = "tf.Cast"(%2) {Truncate = false} : (tensor<960x960x3x512xi8>) -> tensor<960x960x3x512xi32>
    %4 = "tf.Sub"(%3, %arg5) : (tensor<960x960x3x512xi32>, tensor<512xi32>) -> tensor<960x960x3x512xi32>
    %5 = "tf.Conv2D"(%1, %4) {dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x2240x2240x3xi32>, tensor<960x960x3x512xi32>) -> tensor<1x2240x1120x512xi32>

// Configure `QuantizationSpecs` to apply `StaticRangePtq` to compute heavy
// units.
constexpr absl::string_view kSpecsStaticRangePtqToComputeHeavy =
    R"pb(specs
         [ {
           matcher { function_name { regex: "^.*(conv|dot|gather).*" } }
           method { static_range_ptq {} }
         }])pb";

class TestLiftQuantizableSpotsAsFunctionsWithQuantizationSpecsPass
    : public impl::

      if (function_name.contains("with_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }
    } else if (function_name.contains("conv2d")) {
      spec->coeff_op_quant_dim[1] = 3;
      if (function_name.contains("with_bias")) {
        spec->biases_params[2] = {{0, 1},
                                  quant::GetUniformQuantizedTypeForBias};
      }