// CHECK-DAG: %[[cst:.*]] = "tf.Const{{.*}} dense<8.000000e+00> : tensor<3x3x3x16xf32>
// CHECK-DAG: %[[cst_0:.*]] = "tf.Const{{.*}} dense<1.200000e+01> : tensor<16xf32>
// CHECK-NEXT: %[[conv:.*]] = "tf.Conv2D"(%arg0, %[[cst]])
// CHECK-NEXT: %[[bias:.*]] = "tf.AddV2"(%[[conv]], %[[cst_0]])
// CHECK-NEXT: return %[[bias]] : tensor<256x8x7x16xf32>
}

// CHECK-LABEL: convaddv2mul

  %conv = "tf.Conv2D"(%dq_input, %dq_weight) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>

         (tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) ->
         tensor<64x3x3x3xf32>
    %conv = "tfl.conv_2d"(%input_act, %w, %bias)

    but if it is supported, it will be rewritten as:

    %q_w = "tfl.pseudo_qconst"() {
         qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
    %conv = "tfl.conv_2d"(%input_act, %q_w, %bias)

Michael Levesque-Dion <******@****.***> 1706075999 -0800

                return true;
            }
            if (moduleConfiguration.equals("*")) {
                boolean include = true;
                for (String conf2 : dependencyConfigurations) {
                    if (conf2.startsWith("!") && conf2.substring(1).equals(configName)) {
                        include = false;
                        break;
                    }
                }

  // CHECK: %[[CONV:.+]] = mhlo.convolution(%[[INPUT]], %[[NEW_FILTER]]) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {stride = [1, 1], pad = {{\[\[}}0, 0], [0, 0]], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<?x256x256x3xf32>, tensor<1x1x3x2xf32>) -> tensor<?x256x256x2xf32>
  // CHECK: %[[SHAPE:.+]] = shape.shape_of %[[CONV]] : tensor<?x256x256x2xf32> -> tensor<4xindex>

  [(HasRankOf<1> $add_rhs_value),
   (HasEqualElementSize<[-1], [0]> $conv_out, $add_rhs)], [], (addBenefit -1)>;

// Convert conv+sub+mul pattern to conv+mul+add.
// (conv - sub) * mul -> conv * mul + (-sub) * mul
//
// This is needed to support Conv+BatchNorm pattern from Jax models converted
// using jax2tf w/o native serialization. Note that Jax2tf patterns always

// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[ARG1]], %[[ARG2]])
// CHECK-SAME: (tensor<1x3x4x3xf32>, tensor<2x3x3x2x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>) -> tensor<1x3x4x2xf32>
// CHECK: return %[[CONV]]

// -----

// Test that per-channel weight-only quantized dot_general op is produced when

    func.return %7 : tensor<1x112x112x32x!quant.uniform<u8:f32, 1.0>>

// CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %arg1, %arg2)
// CHECK-SAME: -> tensor<1x112x112x32x!quant.uniform<u8:f32, 0.0078431372549019607:128>>
// CHECK: %[[cst:.*]] = "tfl.pseudo_qconst"()
// CHECK: %[[add:.*]] = tfl.add(%[[conv]], %[[cst]])
// CHECK-SAME: -> tensor<1x112x112x32x!quant.uniform<u8:f32, 1.000000e+00>>
// CHECK: return %[[add]]
}

        def results2 = new Object()

        def store1 = factory.createCachedStore("conf1")
        def store1b = factory.createCachedStore("conf1")
        def store2 = factory.createCachedStore("conf2")

        expect:
        store1.load(() -> results1) == results1
        store1.load(() -> { assert false }) == results1
        store1b.load(() -> { assert false }) == results1