// CHECK: %[[v1:.*]] = "tf.Transpose"(%arg1, %[[cst_1]]) : (tensor<2x4x7x3xf32>, tensor<4xi32>) -> tensor<2x7x3x4xf32>
  // CHECK: "tf.BatchMatMulV2"(%[[v0]], %[[v1]]) <{adj_x = false, adj_y = false}> : (tensor<2x7x5x3xf32>, tensor<2x7x3x4xf32>) -> tensor<2x7x5x4xf32>
}

func.func @einsum_matrixdotprod(%arg0: tensor<2x5x7x3xf32>, %arg1: tensor<7x3x4xf32>) -> tensor<2x5x4xf32> {

// CHECK: "tfl.slice"

// -----

func.func @fullyConnectedToConv(%arg0: tensor<384x384xf32>, %arg1: tensor<512x384xf32>, %arg2: tensor<512xf32>) -> tensor<384x512xf32> {
  %0 = "tfl.fully_connected"(%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<384x384xf32>, tensor<512x384xf32>, tensor<512xf32>) -> tensor<384x512xf32>
  func.return %0: tensor<384x512xf32>
}

  %2 = "tfl.select"(%cst_false, %arg0, %arg1) : (tensor<1x2x3x4xi1>, tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32>
  %3 = "tfl.select_v2"(%cst_false, %arg0, %arg1) : (tensor<1x2x3x4xi1>, tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32>
  func.return %0, %1, %2, %3 : tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>

    %cst_0 = "tf.Const"() {value = dense<[-2.000000e+00, 3.000000e+00]> : tensor<2xf32>} : () -> tensor<2xf32>

    %cst_0 = "tf.Const"() {value = dense<[-2.000000e+00, 3.000000e+00]> : tensor<2xf32>} : () -> tensor<2xf32>
    %0 = "quantfork.qcast"(%cst) : (tensor<2x2x3x2xf32>) -> tensor<2x2x3x2x!quant.uniform<i8<-127:127>:f32:3, {4.000000e-03,5.000000e-03}>>

// -----

func.func @testPReluWrongOutputShape(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<2x3x4xf32>) -> tensor<1x2x3x5xf32> {
  // expected-error @+1 {{'tfl.prelu' op result type '1x2x3x5' not broadcast compatible with broadcasted operands's shapes '1x2x3x4'}}
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<1x2x3x4xf32>, tensor<2x3x4xf32>) -> tensor<1x2x3x5xf32>
  func.return %0 : tensor<1x2x3x5xf32>
}

// -----

// CHECK:           %[[VAL_8:.*]] = "tf.Select"(%[[VAL_2]], %[[VAL_3]], %[[VAL_7]]) : (tensor<1x2x3x4xi1>, tensor<1x2x3x4xf32>, tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32>
// CHECK:           return %[[VAL_8]] : tensor<1x2x3x4xf32>
// CHECK:         }
func.func @sign(%arg0: tensor<1x2x3x4xf32>, %arg1: tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32> {

  %fq = "tf.FakeQuantWithMinMaxVarsPerChannel"(%in, %mini, %maxi) {num_bits = 4, narrow_range = true} : (tensor<3x3x3x4xf32>, tensor<4xf32>, tensor<4xf32>) -> tensor<3x3x3x4xf32>
  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x4xf32>) -> tensor<256x8x7x4xf32>
  func.return %rst : tensor<256x8x7x4xf32>

// -----

// CHECK-LABEL: func @sign
// CHECK-SAME: [[ARG:%arg.*]]: tensor<1x2x3x4xf32>
func.func @sign(%arg0: tensor<1x2x3x4xf32>) -> tensor<1x2x3x4xf32> {
  // CHECK: [[SIGN:%.*]] = mhlo.sign [[ARG]]
  // CHECK: return [[SIGN]] : tensor<1x2x3x4xf32>
  %0 = "tf.Sign"(%arg0) : (tensor<1x2x3x4xf32>) -> (tensor<1x2x3x4xf32>)
  func.return %0 : tensor<1x2x3x4xf32>
}

// -----

// CHECK-LABEL: func @sign_dynamic

  //
  // For example,
  //
  //   %shape  = tf.Shape(%arg)                   // %arg: tensor<?x2x3x1xf32>
  //   %height = tf.StridedSlice(%shape, 1, 2, 1)
  //
  // In this case %height can be replaced with a constant 2.
  //
  // Or,
  //
  //   %shape  = tf.Shape(%arg)                   // %arg: tensor<?x2x3x1xf32>
  //   %spatial_shape = tf.StridedSlice(%shape, 1, 3, 1)
  //