%0 = stablehlo.constant dense<2.000000e+00> : tensor<4x2x3x3xf32>  // weight
  %1 = stablehlo.constant dense<3.000000e+00> : tensor<4xf32>  // bias
  %2 = stablehlo.constant dense<0.000000e+00> : tensor<1x4x5x5xf32>  // relu
  %3 = stablehlo.broadcast_in_dim %1, dims = [1] : (tensor<4xf32>) -> tensor<1x4x5x5xf32>

  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,
  LSH_PROJECTION = 15,
  LSTM = 16,
  MAX_POOL_2D = 17,
  MUL = 18,
  RELU = 19,
  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,

  %2 = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
  %3 = "tfl.conv_2d"(%0, %1, %2) {
    dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32,
    fused_activation_function = "RELU", padding = "VALID",
    stride_h = 1 : i32, stride_w = 1 : i32} : (
      tensor<?x5x5x2xf32>, tensor<3x5x5x2xf32>, tensor<3xf32>) -> tensor<?x1x1x3xf32>
  %4 = "quantfork.stats"(%3) {

// CHECK: return %[[MAX]] : tensor<?x28x28x16xf32>
// CHECK: }

// -----

// Because the operand of shape_of is other than the target conv,
// should not match conv relu dynamic pattern.

// CHECK-LABEL: @conv_with_relu_dynamic_shape_not_same_op_fn(
// CHECK-SAME:                    %[[ARG_0:.*]]: tensor<?x28x28x1xf32>

    data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 9.99999974E-5 : f32, explicit_paddings = [], filter_format = "HWIO", fused_ops = ["BiasAdd", "Relu"], leakyrelu_alpha = 2.000000e-01 : f32, num_args = 2 : i64, operandSegmentSizes = array<i32: 1, 1, 2, 2>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true

  %3 = "tfl.reshape"(%1, %2) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<1x128x128xf32>, tensor<2xi32>) -> tensor<128x128xf32>
  %4 = "tfl.relu"(%3) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<128x128xf32>) -> tensor<128x128xf32>
  %5 = "tfl.pseudo_const"() {value = dense<[1, 128, 128]> : tensor<3xi32>} : () -> tensor<3xi32>

// RUN: stablehlo-quant-opt %s -stablehlo-merge-fusion-with-dequantize -split-input-file -verify-diagnostics | FileCheck %s

// Merge fusion with dequantize for relu case.

module attributes {tf_saved_model.semantics} {
  // CHECK-LABEL: func.func private @merge_relu_fusion
  func.func private @merge_relu_fusion(%arg0: tensor<1x4xf32>) -> tensor<1x3xf32> {

  HASHTABLE_LOOKUP = 10,
  L2_NORMALIZATION = 11,
  L2_POOL_2D = 12,
  LOCAL_RESPONSE_NORMALIZATION = 13,
  LOGISTIC = 14,
  LSH_PROJECTION = 15,
  LSTM = 16,
  MAX_POOL_2D = 17,
  MUL = 18,
  RELU = 19,
  // NOTE(aselle): RELU_N1_TO_1 used to be called RELU1, but it was renamed
  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,

      padding: str = 'SAME',
      has_func_alias: bool = False,
  ) -> module.Module:
    class ConvModel(module.Module):
      """A simple model with a single conv2d, bias and relu."""

      def __init__(self):
        self.out_channel_size = filter_shape[-1]

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

   range for the fused activation `act` with the quantization defined by the
   `scale` and `zero point`. Currently, the allowed activations are
   `NONE`, `RELU`, `RELU6` and `RELU_N1_TO_1`.

    Example:

    ```mlir
    %3, %4 = tfr.quant_act_range(%2, %1, %0) :
        (tfr.attr, float, i64) -> (tfr.tensor, tfr.tensor)
    ```
  }];