%2 = "quantfork.stats"(%1) {layerStats = dense<[5.00000000e-6, 7.00000000e-1]> : tensor<2xf32>} : (tensor<2x2x3xf32>) -> tensor<2x2x3xf32>
    return %2 : tensor<2x2x3xf32>
  }
// CHECK: func.func private @quantize_dot_general_batch_per_tensor_quantized_fn(%[[ARG_0:.+]]: tensor<2x2x2xf32>) -> tensor<2x2x3xf32> attributes {tf._original_func_name = "main_0"}

// CHECK:  return %[[RESULT]]
}

func.func @ConvertSqueezeToReshapeWithDynamicDimension2(%arg0: tensor<?x1x8x3xf32>) -> tensor<1x8x3xf32> {
  %0 = "tfl.squeeze"(%arg0) {squeeze_dims = [0]}: (tensor<?x1x8x3xf32>) -> tensor<1x8x3xf32>
  func.return %0: tensor<1x8x3xf32>

// CHECK-LABEL: ConvertSqueezeToReshapeWithDynamicDimension2

  %0 = "tfl.var_handle"() {container = "", shared_name = "states"} : () -> tensor<!tf_type.resource<tensor<1x2x3xf32>>>
  %1 = "tfl.read_variable"(%0) : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<1x2x3xf32>
  %2 = "tfl.quantize"(%1) {qtype = tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>>} : (tensor<1x2x3xf32>) -> tensor<1x2x3x!quant.uniform<u8<1:255>:f32, 0.047244094488188976:128>>

  // CHECK: [[Result:%.*]] = "tf.Case"(%arg0, %arg1)
  // CHECK-SAME: branches = [@tf.CaseRegion_branch0{{.*}}, @tf.CaseRegion_branch1{{.*}}]
  // CHECK-SAME: is_stateless = false
  %0 = "tf.CaseRegion"(%arg0) ({
    %1 = "tf.Cast"(%arg1) {Truncate = false} : (tensor<!tf_type.resource<tensor<1x2x3xf32>>>) -> tensor<!tf_type.resource>

// Test scatter into existing tensor list.

// CHECK-LABEL: func @main
// CHECK-SAME: (%[[ARG0:.*]]: tensor<10x8x9xf32>, %[[ARG1:.*]]: tensor<5xi32>, %[[ARG2:.*]]: tensor<5x8x9xf32>) -> tensor<10x8x9xf32>
func.func @main(%arg0: tensor<10x8x9xf32>, %arg1: tensor<5xi32>, %arg2: tensor<5x8x9xf32>) -> tensor<10x8x9xf32> {
  %elem_shape = "tf.Const"() {value = dense<[8, 9]> : tensor<2xi32>} : () -> tensor<2xi32>

}

// CHECK-LABEL: @StridedSliceEllipsisAndNewAxisMaskBothSet
func.func @StridedSliceEllipsisAndNewAxisMaskBothSet(%arg0: tensor<6x7x8xf32>) -> tensor<2x1x7x8x1xf32> {
  %begin = arith.constant dense<0> : tensor<4xi32>
  %end = arith.constant dense<[2,3,4,5]> : tensor<4xi32>
  %step = arith.constant dense<1> : tensor<4xi32>

}

// -----

func.func @trivial_dynamic_update_slice_wrong_update_shape(%arg0: tensor<2x7x14xf32>, %arg1: tensor<2x7x7xf32>) -> tensor<2x7x14xf32> {
  %0 = arith.constant dense<0> : tensor<3xi32>
  %1 = "tfl.dynamic_update_slice"(%arg0, %arg1, %0) : (tensor<2x7x14xf32>, tensor<2x7x7xf32>, tensor<3xi32>) -> tensor<2x7x14xf32>
  // CHECK: "tfl.dynamic_update_slice"
  func.return %1 : tensor<2x7x14xf32>
}

// CHECK:           return %[[VAL_7]] : tensor<5x7x9xi32>
// CHECK:         }
func.func @convert_iota_3d() -> tensor<5x7x9xi32> {
  %0 = "mhlo.iota"() <{ iota_dimension = 1 : i64 }> : () -> tensor<5x7x9xi32>
  func.return %0 : tensor<5x7x9xi32>
}

// CHECK-LABEL:   func @convert_avgpool_valid(

func.func @size_to_prod_shape_i32(%arg0 : tensor<1x?x2x3xf32>) -> tensor<i32> {
  %0 = "tf.Size"(%arg0) : (tensor<1x?x2x3xf32>) -> tensor<i32>
  func.return %0 : tensor<i32>
  // CHECK: %[[CONSTANT:.*]] = "tf.Const"() <{value = dense<0> : tensor<i32>}> : () -> tensor<i32>
  // CHECK: %[[SHAPE:.*]] = "tf.Shape"(%arg0) : (tensor<1x?x2x3xf32>) -> tensor<4xi32>

func.func @testDilate(%arg0: tensor<3x4x5xf32>) -> tensor<5x7x9xf32> {
  // CHECK: "tfl.dilate"(%arg0, %cst, %cst_0)
  %cst = arith.constant dense<1> : tensor<3xi32>
  %cst_0 = arith.constant dense<-1.0> : tensor<f32>
  %0 = "tfl.dilate"(%arg0, %cst, %cst_0) : (tensor<3x4x5xf32>, tensor<3xi32>, tensor<f32>) -> tensor<5x7x9xf32>
  func.return %0 : tensor<5x7x9xf32>
  // CHECK: return %0 : tensor<5x7x9xf32>