[(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

  using OpRewritePattern<TFL::SplitVOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(TFL::SplitVOp splitv_op,
                                PatternRewriter& rewriter) const override;
};

// Ensure bias for conv2d op.
struct EnsureBiasForConv2d : public OpRewritePattern<TFL::Conv2DOp> {
  using OpRewritePattern<TFL::Conv2DOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(TFL::Conv2DOp conv_op,

// CHECK: %[[DEQUANTIZE:.*]] = "quantfork.dcast"(%[[QUANTIZE]])
// CHECK: %[[CONV:.*]] = "tf.Conv2D"(%arg0, %[[DEQUANTIZE]])
// CHECK: return %[[CONV]]
}

// CHECK-LABEL: perChannelFakeQuantWithConv2D
func.func @perChannelFakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) {
^bb0(%arg: tensor<256x32x32x3xf32>) :

// CHECK: %[[cast:.*]] = "tf.Cast"(%[[cst]]) <{Truncate = false}> : (tensor<2x3x3x2xbf16>) -> tensor<2x3x3x2xf32>
// CHECK: %[[conv:.*]] = "tf.Conv2D"(%[[ARG0]], %[[cast]])
// CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[conv]]) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
// CHECK: return %[[identity]] : tensor<1x3x2x2xf32>

// -----

// Tests that `tf.BatchFunction` is inlined.

      name: "cond"
      input_arg {
        name: "cond"
        type: DT_INT32
      }
      input_arg {
        name: "cond1"
        type: DT_FLOAT
      }
      output_arg {
        name: "cond2"
        type: DT_BOOL
      }
    }
    node_def {
      name: "Const"
      op: "Const"
      attr {
        key: "dtype"
        value {
          type: DT_INT32
        }
      }

	s [2]string
}

func f(a T) { // ERROR "live at entry to f: a$"
	var e interface{} // ERROR "stack object e interface \{\}$"
	func() {          // ERROR "live at entry to f.func1: &e a$"
		e = a.s // ERROR "live at call to convT: &e$" "stack object a T$"
	}()
	// Before the fix, both a and e were live at the previous line.
	_ = e

  // cuDNN prefers NCHW data format for spatial convolutions in f16 before
  // compute capability 7.0 (NVIDIA Tensor Cores).

  // CHECK: "tf.Conv2D"(%[[INPUT_TRANSPOSE:[0-9]*]], %arg1)
  // CHECK-SAME: data_format = "NCHW"
  %0 = "tf.Conv2D"(%input, %filter)
       {
         data_format = "NHWC",
         padding = "VALID",
         strides = [1, 1, 1, 1]

    }

    def "renders only classpath build script configuration"() {
        given:
        project.buildscript.configurations.create("conf1")
        project.buildscript.configurations.create("conf2")

        when:
        task.setRenderer(renderer)
        task.generate()

        then:
        task.reportModel.get().configuration.name == "classpath"
    }

// 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

 ReadVariableOp 2
 VarHandleOp 435
 _Retval 2
cluster 0 size 2178
 Add 17
 AddN 72
 ArgMax 1
 AssignAddVariableOp 1
 AssignSubVariableOp 106
 BiasAdd 1
 BiasAddGrad 1
 Cast 3
 Const 357
 Conv2D 53
 Conv2DBackpropFilter 53
 Conv2DBackpropInput 52
 DivNoNan 1
 Equal 1
 FusedBatchNorm 53
 FusedBatchNormGrad 53
 Identity 2
 MatMul 3
 MaxPool 1
 MaxPoolGrad 1
 Mean 1
 Mul 164
 Pad 1