// attribute which is shared.
  bool AreFuseCompatible(Conv2DOp conv, BiasAddOp bias_add,
                         PatternRewriter &rewriter) const override {
    // Verify that the data formats match and are valid for fusion.
    if (conv.getDataFormat() != bias_add.getDataFormat()) {
      (void)rewriter.notifyMatchFailure(conv, [&](Diagnostic &diag) {

    key: "narrow_range"
    value {
      b: true
    }
  }
  attr {
    key: "num_bits"
    value {
      i: 8
    }
  }
}
node {
  name: "BoxPredictor_4/ClassPredictor/Conv2D"
  op: "Conv2D"
  input: "input"
  input: "BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel"
  attr {
    key: "T"
    value {
      type: DT_FLOAT
    }
  }
  attr {

// CHECK: return %0 : tensor<*xf32>
}

// -----

module {
  func.func @conv2d(%arg0: tensor<1x3x4x3xf32>) -> (tensor<*xf32>) {
    %cst_0 = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>

// CHECK-NOT: func private @internal_conv2d_fn
// CHECK-NOT: func private @internal_matmul_fn
// CHECK: func private @quantized_conv2d_with_bias_fn
// CHECK-SAME: tf_quant.quantized_ops = ["Conv2D", "BiasAdd"]
// CHECK: func private @quantized_conv2d_with_bias_and_relu_fn
// CHECK: func private @quantized_conv2d_with_bias_and_relu6_fn
// CHECK: func private @quantized_conv2d_fn

  %b = arith.constant dense<-1.23697901> : tensor<64xf32>
  %conv = "tfl.conv_2d"(%0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>) -> tensor<1x112x112x64xf32>

","statusText","finalText","crossDomain","host","hasContent","ifModified","headers","beforeSend","success","send","nativeStatusText","responses","isSuccess","response","modified","ct","finalDataType","firstDataType","ajaxHandleResponses","conv2","current","conv","dataFilter","throws","ajaxConvert","getJSON","getScript","text script","wrapAll","firstElementChild","wrapInner","htmlIsFunction","unwrap","visible","xhr","XMLHttpRequest","xhrSuccessStatus","0","1223","xhrSupported","cors","errorCallba...

        conf1.outgoingEdges >> [dep(selector2, 2, 3)]

        def conf2 = node(2, "org", "dep1", "1.0")
        def selector3 = selector(12, "org", "dep3", "1.0")
        conf2.outgoingEdges >> [dep(selector3, 2, 4)]

        builder.start(root)

        builder.visitNode(root)
        builder.visitNode(conf1)
        builder.visitNode(conf2)
        builder.visitNode(node(3, "org", "dep2", "1.0"))

				cmplw := ir.Node(ir.NewIndexExpr(base.Pos, cmpl, ir.NewInt(base.Pos, i)))
				cmplw = typecheck.Conv(cmplw, elemType) // convert to unsigned
				cmplw = typecheck.Conv(cmplw, convType) // widen
				cmprw := ir.Node(ir.NewIndexExpr(base.Pos, cmpr, ir.NewInt(base.Pos, i)))
				cmprw = typecheck.Conv(cmprw, elemType)
				cmprw = typecheck.Conv(cmprw, convType)
				// For code like this:  uint32(s[0]) | uint32(s[1])<<8 | uint32(s[2])<<16 ...

		case fmtTypeID, fmtTypeIDName:
			if verb == 'S' {
				tconv2(b, t.Elem(), 'S', mode, visited)
				return
			}
		}
		tconv2(b, t.Elem(), 'v', mode, visited)

	case TARRAY:
		b.WriteByte('[')
		b.WriteString(strconv.FormatInt(t.NumElem(), 10))
		b.WriteByte(']')
		tconv2(b, t.Elem(), 0, mode, visited)

	case TSLICE:
		b.WriteString("[]")
		tconv2(b, t.Elem(), 0, mode, visited)

	case TCHAN:

  // 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>