%cst_1 = arith.constant dense<6.000000e+00> : tensor<2x1x3x3xf32>
  %0 = "tfl.quantize"(%cst_1) {qtype = tensor<2x1x3x3x!quant.uniform<i8<-127:127>:f32:0, {6.587140e-03,1.888450e-02}>>} : (tensor<2x1x3x3xf32>) -> tensor<2x1x3x3x!quant.uniform<i8<-127:127>:f32:0, {6.587140e-03,1.888450e-02}>>
  %1 = "tfl.dequantize"(%0) : (tensor<2x1x3x3x!quant.uniform<i8<-127:127>:f32:0, {6.587140e-03,1.888450e-02}>>) -> tensor<2x1x3x3xf32>

// CHECK:           })
// CHECK-SAME:        -> tensor<2x4x6x7xf32>
// CHECK:           %[[RESULT:.*]] = mhlo.divide %[[DIVIDEND]], %[[DIVISOR]] : tensor<2x4x6x7xf32>
// CHECK:           return %[[RESULT]] : tensor<2x4x6x7xf32>
// CHECK:         }
func.func @avgpool_same_padding(%arg0: tensor<2x12x21x7xf32>) -> tensor<2x4x6x7xf32> {

  %arg1: tensor<f32>
) -> tensor<2x4x3x3xf32> {
  %0 = "stablehlo.reduce_window"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<f32>, %arg3: tensor<f32>):
    %1 = stablehlo.maximum %arg2, %arg3 : tensor<f32>
    stablehlo.return %1 : tensor<f32>
  }) {window_dimensions = array<i64: 1, 3, 4, 1>, window_strides = array<i64: 1, 2, 3, 1>} : (tensor<2x9x10x3xf32>, tensor<f32>) -> tensor<2x4x3x3xf32>
  return %0 : tensor<2x4x3x3xf32>
}

  %0 = "tf.Gather"(%arg0, %arg1) : (tensor<2x3x6xf32>, tensor<4x5xi32>) -> tensor<4x5x3x6xf32>
  func.return %0 : tensor<4x5x3x6xf32>

// CHECK-LABEL:gatherHigherRankIndices
// CHECK:  "tfl.gather"(%arg0, %arg1) <{axis = 0 : i32, batch_dims = 0 : i32}> : (tensor<2x3x6xf32>, tensor<4x5xi32>) -> tensor<4x5x3x6xf32>
}

//
// ```
// %0 = stablehlo.constant dense<3>
// %1 = stablehlo.constant dense<4>
// %2 = stablehlo.constant dense<2>
// %3 = stablehlo.convolution(%%arg0, %%arg1) :
//          (tensor<?x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<?x3x4x2xf32>
// %4 = stablehlo.get_dimension_size %3, dim = 0 :
//          (tensor<?x3x4x2xf32>) -> tensor<i32>
// %5 = stablehlo.reshape %4 :
//          (tensor<i32>) -> tensor<1xi32>

  // CHECK: %0 = "tfl.conv_2d"(%arg0, %arg1, %cst)
}

// CHECK-LABEL: fuse4DAddIntoConv2d
func.func @fuse4DAddIntoConv2d(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<2x3x3x3xf32>) -> tensor<256x32x32x2xf32> {
  %cst = arith.constant dense<[[[[1.0, 2.0]]]]> : tensor<1x1x1x2xf32>
  %cst_0 = arith.constant dense<[1.0, 2.0]> : tensor<2xf32>
  %0 = "tfl.conv_2d"(%arg0, %arg1, %cst_0) {