%3 = stablehlo.add %2, %1 : tensor<1x4x5x5xf32>
  return %3 : tensor<1x4x5x5xf32>
}
// CHECK-DAG: %[[WEIGHT_CONST:.+]] = stablehlo.constant {{.*}} : tensor<3x3x2x4xf32>
// CHECK-DAG: %[[BIAS_CONST:.+]] = stablehlo.constant {{.*}} : tensor<1x5x5x4xf32>
// CHECK-DAG: %[[TRANSPOSE_0:.+]] = stablehlo.transpose %[[ARG]], dims = [0, 2, 3, 1] : (tensor<1x2x5x5xf32>) -> tensor<1x5x5x2xf32>

    }> {
      _collective_manager_ids = [], device = ""
    } : (tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32>
    func.return %0: tensor<1x2x2x3xf32>
  }

  func.func private @some_func(%arg0: tensor<1x2x2x3xf32>) -> tensor<1x2x2x3xf32> attributes {tf._noinline = true} {
    return %arg0 : tensor<1x2x2x3xf32>
  }
}

// CHECK: module
// CHECK: tf.StatefulPartitionedCall
// CHECK: func.func private @some_func

  %1 = "tf.Maximum"(%0, %cst_0) : (tensor<1x3x4x2xf32>, tensor<f32>) -> tensor<1x3x4x2xf32>
  %2 = "tf.Minimum"(%1, %cst_1) : (tensor<1x3x4x2xf32>, tensor<f32>) -> tensor<1x3x4x2xf32>
  func.return %2 : tensor<1x3x4x2xf32>
// CHECK-DAG: %[[CONST_0:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<1x1x3x2xf32>}> : () -> tensor<1x1x3x2xf32>

    has_token_input_output = false, module = "", platforms = [],
    version = 5 : i64
  } : (tensor<1x3x2x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x2x2x2xf32>
  return %0 : tensor<1x2x2x2xf32>
}

// CHECK-LABEL: func.func @qdq_for_conv_weight_empty
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32>
// CHECK: %[[CST:.+]] = "tf.Const"() <{value = dense<3.000000e-01> : tensor<2x3x3x2xf32>}> : () -> tensor<2x3x3x2xf32>

    %2 = "tf.Relu"(%1) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
    %3 = "tf.FakeQuantWithMinMaxArgs"(%2) {device = "", max = 4.000000e-01 : f32, min = -3.000000e-01 : f32, narrow_range = false, num_bits = 8 : i64} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>

  %2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x3x2x2xbf16>) -> tensor<1x3x2x2xf32>
  %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32>
  return %3 : tensor<1x3x2x2xf32>
}

// CHECK: func @cast_bf16_conv_to_fp32

    %6 = "quantfork.stats"(%5) {layerStats = dense<[-1.31055, 2.62842]> : tensor<2xf32>} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
    %cst_min = stablehlo.constant dense<0.0> : tensor<f32>
    %cst_max = stablehlo.constant dense<6.0> : tensor<f32>
    %7 = "stablehlo.clamp"(%cst_min, %6, %cst_max) {device = ""} : (tensor<f32>, tensor<1x2x2x2xf32>, tensor<f32>) -> tensor<1x2x2x2xf32>

    %0 = "tf.Cast"(%arg0) : (tensor<1x3x2x2xi8>) -> tensor<1x3x2x2xi32>
    %1 = "tf.Sub"(%0, %arg2) : (tensor<1x3x2x2xi32>, tensor<i32>) -> tensor<1x3x2x2xi32>
    %2 = "tf.Cast"(%1) : (tensor<1x3x2x2xi32>) -> tensor<1x3x2x2xf32>
    %3 = "tf.Mul"(%2, %arg1) : (tensor<1x3x2x2xf32>, tensor<f32>) -> tensor<1x3x2x2xf32>
    return %3 : tensor<1x3x2x2xf32>
  }

// CHECK: %cst_2 = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>
// CHECK: %4 = "tfl.transpose"(%3, %cst_2) : (tensor<1x2x2x1xf32>, tensor<4xi32>) -> tensor<1x1x2x2xf32>
// CHECK: return %4 : tensor<1x1x2x2xf32>

func.func @avg_pool2d_6(%arg0: tensor<1x1x1x7xf32>) -> (tensor<1x1x1x2xf32>) {

// CHECK: return %[[DEQUANTIZED]] : tensor<*xf32>
}

// -----

module {
  func.func @not_quantize_matmul_without_const(%arg0: tensor<1x2x2x2xf32>, %arg1: tensor<2x1024xf32>) -> (tensor<*xf32>) {
    %arg0_identity = "tf.Identity"(%arg0) {device = ""} : (tensor<1x2x2x2xf32>) -> tensor<1x2x2x2xf32>
    %arg1_identity = "tf.Identity"(%arg1) {device = ""} : (tensor<2x1024xf32>) -> tensor<2x1024xf32>