// CHECK: return %[[identity]] : tensor<1x2x2x6xf32>

func.func @cast_bf16_batch_matmul_v2_to_fp32(%arg0: tensor<1x1x10xf32>) -> (tensor<1x1x2xf32>) {
  %cst = "tf.Const"() {device = "", value = dense<1.000000e+01> : tensor<10x2xbf16>} : () -> tensor<10x2xbf16>
  %0 = "tf.Cast"(%arg0) {Truncate = false, device = ""} : (tensor<1x1x10xf32>) -> tensor<1x1x10xbf16>

//CHECK:func.func private @slice(%arg0: tensor<160x20x1xf32>) -> tensor<1x1x1xf32> {
//CHECK-NEXT: %0 = "vhlo.slice_v1"(%arg0) <{limit_indices = #vhlo.tensor_v1<dense<0> : tensor<3xi64>>, start_indices = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>, strides = #vhlo.tensor_v1<dense<1> : tensor<3xi64>>}> : (tensor<160x20x1xf32>) -> tensor<1x1x1xf32> 
//CHECK-NEXT: return %0 : tensor<1x1x1xf32>
//CHECK-NEXT:}

func.func @EliminateReduceOpsFloat(%arg: tensor<1x2x1x3xf32>, %arg_scalar: tensor<f32>, %arg_unknown: tensor<?xf32>, %axis_unknown: tensor<?xi32>) -> (tensor<f32>, tensor<f32>, tensor<2x1x3xf32>, tensor<1x2x1x3xf32>, tensor<1x1x1x3xf32>, tensor<1x1x3xf32>, tensor<1x2x3xf32>, tensor<1x2x1x3xf32>, tensor<1x2x1xf32>, tensor<1x2x1x1xf32>, tensor<?xf32>) {
  %axis_0 = arith.constant dense<0> : tensor<1xi32>

func.func @testPadUnknownPaddings(tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<2x1x3xf32>, %arg1: tensor<*xi32>):
  %0 = "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>

  // CHECK-LABEL: testPadUnknownPaddings
  // CHECK:  "tfl.pad"(%arg0, %arg1) : (tensor<2x1x3xf32>, tensor<*xi32>) -> tensor<?xf32>
  // CHECK:  return
}

// -----

  %0 = "mhlo.broadcast_in_dim"(%arg0) <{broadcast_dimensions = dense<[1, 2, 3]> : tensor<3xi64>, name = "broadcast.0"}> : (tensor<8x1x16xf32>) -> tensor<3x8x8x16xf32>
  func.return %0 : tensor<3x8x8x16xf32>
}

// CHECK-LABEL:   func @broadcast_in_dim_general_case(
// CHECK-SAME:                                        %[[VAL_0:.*]]: tensor<3x1x16xf32>) -> tensor<3x8x8x16xf32> {

  func.return %0 : tensor<2x2xf16>
}

// -----

// CHECK-LABEL: squeeze
func.func @squeeze(%arg0: tensor<1x1x10xf32>) -> tensor<1x10xf32> {
  // CHECK: mhlo.reshape
  %0 = "tf.Squeeze"(%arg0) : (tensor<1x1x10xf32>) -> tensor<1x10xf32>
  func.return %0 : tensor<1x10xf32>
}

// -----

// CHECK-LABEL: squeeze_ranked