(BinBroadcastDimensions $one, $features))))>;

//===----------------------------------------------------------------------===//
// Relu op patterns.
//===----------------------------------------------------------------------===//

// TODO(hinsu): Make these patterns to TF to TF lowering. Relu6 lowering will
// require HLO canonicalization of min and max on a tensor to ClampOp.

  let hasFolder = 1;
}

def TFL_ReluOp: TFL_Op<"relu", [
    PredOpTrait<"x and y must have same element type",
      TFL_TCresVTEtIsSameAsOp<0, 0>>,
    Pure,
    QuantizableResult,
    SameOperandsAndResultShape]> {
  let summary = "Relu operator";

  let description = [{
    Element-wise Relu operator
      x -> max(0, x)
  }];

  // CHECK: %0 = "tf.Relu6"(%arg0) {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<4xf32>) -> tensor<4xf32>
  // CHECK: return %0
  %cst_6 = arith.constant dense<6.000000e+00> : tensor<f32>
  %0 = "tf.Minimum"(%arg0, %cst_6) : (tensor<4xf32>, tensor<f32>) -> tensor<4xf32>

  // CHECK: "RELU"
  %1 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU"} : tensor<4xi32>
  // CHECK: "RELU_N1_TO_1"
  %2 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU_N1_TO_1"} : tensor<4xi32>
  // CHECK: "RELU6"
  %3 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU6"} : tensor<4xi32>
  // CHECK: "TANH"

      dilations: Sequence[int] = (1, 1, 1, 1),
      padding: str = 'SAME',
  ):
    class DepthwiseConvModel(module.Module):
      """A simple model with a single depthwise conv2d, bias and relu."""

      def __init__(self):
        self.out_channel_size = filter_shape[2] * filter_shape[3]

        # This ensures filters will have different value range per out channel
        self.filters = np.stack(

    %1 = "tf.Relu"(%0) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
    return %1 : tensor<1x3x2x2xf32>
  }

  return llvm::StringSwitch<tflite::ActivationFunctionType>(str)
      .Case("NONE", tflite::ActivationFunctionType_NONE)
      .Case("RELU", tflite::ActivationFunctionType_RELU)
      .Case("RELU_N1_TO_1", tflite::ActivationFunctionType_RELU_N1_TO_1)
      .Case("RELU6", tflite::ActivationFunctionType_RELU6)
      .Case("TANH", tflite::ActivationFunctionType_TANH)
      .Case("SIGN_BIT", tflite::ActivationFunctionType_SIGN_BIT);

  %5 = "quantfork.stats"(%4) {layerStats = dense<[-56.2916565, 122.922478]> : tensor<2xf32>} : (tensor<1x4xf32>) -> tensor<1x4xf32>
  %6 = "tfl.svdf"(%0, %1, %2, %3, %5) {fused_activation_function = "RELU", rank = 1 : i32} : (tensor<1x3xf32>, tensor<2x3xf32>, tensor<2x1xf32>, tensor<2xf32>, tensor<1x4xf32>) -> tensor<1x2xf32>

	{"1.38.1", rel2, "go1.39.2"},
	{"1.38.1", relRC, "go1.39.2"},
	{"1.39", rel0, "go1.39.2"},
	{"1.39", rel2, "go1.39.2"},
	{"1.39", relRC, "go1.39.2"},
	{"1.39.2", rel0, "go1.39.2"},
	{"1.39.2", rel2, "go1.39.2"},
	{"1.39.2", relRC, "go1.39.2"},
	{"1.39.3", rel0, "go1.40.0"},
	{"1.39.3", rel2, "go1.40.2"},
	{"1.39.3", relRC, "go1.40.2"},
	{"1.40", rel0, "go1.40.0"},
	{"1.40", rel2, "go1.40.2"},

' ##################################

!define Rel_(e_alias1, e_alias2, e_label, e_direction="") e_alias1 e_direction e_alias2 : "===e_label"
!define Rel_(e_alias1, e_alias2, e_label, e_techn, e_direction="") e_alias1 e_direction e_alias2 : "===e_label\n//<size:TECHN_FONT_SIZE>[e_techn]</size>//"

!define Rel(e_from,e_to, e_label) Rel_(e_from,e_to, e_label, "-->")
!define Rel(e_from,e_to, e_label, e_techn) Rel_(e_from,e_to, e_label, e_techn, "-->")