// CHECK-NEXT:  %[[SELU_VAL:.*]] = "tf.Mul"(%[[ELU_VAL]], %[[SCALED_ALPHA]]) : (tensor<1x4x4x3xf32>, tensor<f32>) -> tensor<1x4x4x3xf32>
    // CHECK-NEXT:  %[[RES:.*]] = "tf.SelectV2"(%[[PRED]], %[[SCALED_FEATURES]], %[[SELU_VAL]]) : (tensor<1x4x4x3xi1>, tensor<1x4x4x3xf32>, tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xf32>
    // CHECK-NEXT:  return %[[RES]] : tensor<1x4x4x3xf32>

  // CHECK:  %[[MUL3:.*]] = "tf.Mul"(%[[MEAN]], %[[MUL1]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  // CHECK:  %[[SUB:.*]] = "tf.Sub"(%arg2, %[[MUL3]]) : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xf32>
  // CHECK:  %[[ADD0:.*]] = "tf.Add"(%[[MUL2]], %[[SUB]]) : (tensor<1x1x6x2xf32>, tensor<2xf32>) -> tensor<1x1x6x2xf32>

    %1 = "tf.PartitionedCall"(%arg0, %cst, %cst_0) {_tfl_quant_trait = "fully_quantizable", config = "", config_proto = "", executor_type = "", f = @composite_conv2d_with_bias_and_relu6_fn_1} : (tensor<1x2x2x3xf32>, tensor<2x2x3x2xf32>, tensor<2xf32>) -> tensor<*xf32> loc(callsit...

  // CHECK-SAME: explicit_paddings = [1, 2, 5, 6, 7, 8, 3, 4]
  // CHECK-SAME: padding = "EXPLICIT"
  // CHECK-SAME: strides = [5, 7, 8, 6]
  // CHECK-SAME: (tensor<1x32x32x3xf32>, tensor<1x1x3x8xf32>) -> tensor<1x7x6x8xf32>

  // CHECK: %[[RES_PERM:.*]] = "tf.Const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi64>}>
  // CHECK: %[[RES_TRANSPOSE:[0-9]*]] = "tf.Transpose"(%[[CONV2D]], %[[RES_PERM]])
  // CHECK: return %[[RES_TRANSPOSE]]

  func.return %mm : tensor<1x3x3x3xf32>

// CHECK: %[[mm:.*]] = "tfl.batch_matmul"(%arg0, %arg0) <{adj_x = false, adj_y = true}>
// CHECK: return %[[mm:.*]]

// PerTensor: %[[mm:.*]] = "tfl.batch_matmul"(%arg0, %arg0) <{adj_x = false, adj_y = true}>
// PerTensor: return %[[mm:.*]]
}

// CHECK-LABEL: NotQuantizeConst
// Float16-LABEL: NotQuantizeConst

}

// Test no fold because of the broadcast.
func.func @DontRemoveTrivialMul(%arg0: tensor<1x6x8x1xf32>) -> tensor<1x6x8x1xf32> {
  %0 = "tf.Const"() {value = dense<2.000000e+00> : tensor<f32>} : () -> tensor<f32>
  %1 = "tf.Mul"(%arg0, %0) : (tensor<1x6x8x1xf32>, tensor<f32>) -> tensor<1x6x8x1xf32>
  func.return %1 : tensor<1x6x8x1xf32>
  // CHECK-LABEL: DontRemoveTrivialMul

  %0:2 = "tf.SplitV"(%arg0, %arg1, %arg2) : (tensor<1x4x3x3xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<1x4x2x3xf32>, tensor<1x4x1x3xf32>)
  func.return %0#0 : tensor<1x4x2x3xf32>

  // CHECK-LABEL: splitv
  // CHECK: "tfl.split_v"(%arg0, %arg1, %arg2) <{num_splits = 2 : i32}> : (tensor<1x4x3x3xf32>, tensor<2xi32>, tensor<i32>) -> (tensor<1x4x2x3xf32>, tensor<1x4x1x3xf32>)
}

    // CHECK-NEXT: %[[CMP:.*]] = mhlo.compare GT, %[[INP]], %[[ZERO]], NOTYPE : (tensor<1x4x4x3xf32>, tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xi1>
    // CHECK-NEXT: %[[RES:.*]] = mhlo.select %[[CMP]], %[[INP]], %[[LEAKY]] : tensor<1x4x4x3xi1>, tensor<1x4x4x3xf32>
    // CHECK-NEXT: return %[[RES]] : tensor<1x4x4x3xf32>
    %0 = "tf.LeakyRelu"(%arg0) {alpha = 2.000000e-01 : f32, device = ""} : (tensor<1x4x4x3xf32>) -> tensor<1x4x4x3xf32>

//===--------------------------------------------------------------------===//

func.func @testGatherV2(%arg0: tensor<16x2x3xf32>, %arg1: tensor<16x5xi32>) -> tensor<16x2x5x3xf32> {
  %0 = "tf.Const"() { value = dense<[-1]> : tensor<1xi32> } : () -> tensor<1xi32>
  %1 = "tf.GatherV2"(%arg0, %arg1, %0) {batch_dims = -1 : i64} : (tensor<16x2x3xf32>, tensor<16x5xi32>, tensor<1xi32>) -> tensor<16x2x5x3xf32>
  func.return %1 : tensor<16x2x5x3xf32>
}

// CHECK-LABEL: func @tfAssertTrue
func.func @tfAssertTrue(%arg0: tensor<1x1x6x2xf32>) {
  %t = arith.constant dense<true> : tensor<i1>
  // CHECK-NOT: tf.Assert
  "tf.Assert"(%t, %arg0) {summarize = 3} : (tensor<i1>, tensor<1x1x6x2xf32>) -> ()
  func.return
}

// CHECK-LABEL: func @tfAssertFalse
func.func @tfAssertFalse(%arg0: tensor<1x1x6x2xf32>) {
  %f = arith.constant dense<false> : tensor<i1>
  // CHECK: tf.Assert