%0 = call @XlaCallModule__resize_0(%arg0) : (tensor<1x2x2x10xf32>) -> tensor<1x4x4x10xf32>
  return %0 : tensor<1x4x4x10xf32>
}
func.func private @XlaCallModule__resize_0(%arg0: tensor<1x2x2x10xf32>) -> (tensor<1x4x4x10xf32>) {
  %0 = mhlo.constant dense<2> : tensor<i32>
  %1 = mhlo.constant dense<0> : tensor<i32>
  %2 = mhlo.constant dense<4.000000e+00> : tensor<f32>

// CHECK-NOT: call

// -----

func.func @handles_mhlo_regions() {
  "glue.someop"() : () -> ()
  %4 = mhlo.constant dense<0.0> : tensor<200x10xf32>
  %13 = mhlo.constant dense<-0.0> : tensor<f32>
  %29 = mhlo.reduce(%4 init: %13) applies mhlo.add across dimensions = [1] : (tensor<200x10xf32>, tensor<f32>) -> tensor<200xf32>
  return
}

// CHECK: func @handles_mhlo_regions
// CHECK: call [[name:@[^(]*]](

}

func.func @gatherV2VectorIndices_I64Axis(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> tensor<1x3x5x20xf32> {
  %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi64> } : () -> tensor<1xi64>
  %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi64>) -> tensor<1x3x5x20xf32>

func.func @eliminateExtraSelectLhs(%arg0: tensor<4x2x1xf32>, %arg1: tensor<4x2x1xi1>) -> (tensor<4x2x1xf32>) {
  %cst0 = arith.constant dense<1.0> : tensor<2x2xf32>
  %cst1 = arith.constant dense<2.0> : tensor<2xf32>
  %cst_zeros = arith.constant dense<0.0> : tensor<4x2x1xf32>

  %0 = "tfl.select_v2"(%arg1, %cst_zeros, %arg0) : (tensor<4x2x1xi1>, tensor<4x2x1xf32>, tensor<4x2x1xf32>) -> tensor<4x2x1xf32>

// 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-LABEL: test_gather
func.func @test_gather(%arg0 : tensor<1x2x20xf32>, %arg1 : tensor<3x5xi32>) -> (tensor<1x3x5x20xf32>){
    // expected-remark@+1 {{Cost: 1}}
    %0 = "tf.Const"() { value = dense<[1]> : tensor<1xi32> } : () -> tensor<1xi32>
    // expected-remark@+1 {{Cost: 300}}
    %1 = "tf.GatherV2"(%arg0, %arg1, %0) : (tensor<1x2x20xf32>, tensor<3x5xi32>, tensor<1xi32>) -> tensor<1x3x5x20xf32>

    %0 = "tf.Div"(%arg1, %cst_0) : (tensor<2x2x2xf32>, tensor<f32>) -> tensor<2x2x2xf32>
    %1 = "tf.AddV2"(%0, %cst_1) : (tensor<2x2x2xf32>, tensor<f32>) -> tensor<2x2x2xf32>
    %2 = "tf.Floor"(%1) : (tensor<2x2x2xf32>) -> tensor<2x2x2xf32>
    %3 = "tf.ClipByValue"(%2, %cst_5, %cst_6) : (tensor<2x2x2xf32>, tensor<f32>, tensor<f32>) -> tensor<2x2x2xf32>
    %4 = "tf.Cast"(%3) {Truncate = false} : (tensor<2x2x2xf32>) -> tensor<2x2x2xi8>

// CHECK: Number of dequantize layers added: 1
}


// -----

module {
  func.func @float_einsum(%arg0: tensor<?x64x32xf32>, %arg1: tensor<32x2x16xf32>) -> (tensor<?x64x2x16xf32>) {
    %0 = "tf.Einsum"(%arg0, %arg1) {equation = "abc,cde->abde"} : (tensor<?x64x32xf32>, tensor<32x2x16xf32>) -> tensor<?x64x2x16xf32>
    func.return %0 : tensor<?x64x2x16xf32>
  }

// CHECK-LABEL: func @float_einsum

  >} : (tensor<2x1x512xf32>, tensor<512x13xf32>) -> tensor<2x1x13xf32>
  %2 = "mhlo.dot_general"(%arg2, %arg3) {
    dot_dimension_numbers = #mhlo.dot<
      lhs_contracting_dimensions = [2],
      rhs_contracting_dimensions = [0]
  >} : (tensor<3x1x512xf32>, tensor<512x13xf32>) -> tensor<3x1x13xf32>
  %r = "mhlo.concatenate"(%0, %1, %2) <{dimension = 0 : i64}> : (tensor<1x1x13xf32>, tensor<2x1x13xf32>, tensor<3x1x13xf32>) -> tensor<6x1x13xf32>

  // CHECK: return %[[RESULT]]

  %indices = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
  %0 = "tf.DynamicStitch"(%indices, %arg0) : (tensor<2xi32>, tensor<2x2x2xf32>) -> tensor<2x2x2xf32>
  func.return %0 : tensor<2x2x2xf32>
}

// CHECK-LABEL: func @DynamicStitch_dynamic