func.return %0 : tensor<i32>
}

// -----

// CHECK-LABEL: constant_tensor_vector
func.func @constant_tensor_vector(%arg0: vector<1x2xi32>) -> tensor<1x2xi32> {
  %0 = "tfr.constant_tensor"(%arg0) : (vector<1x2xi32>) -> tensor<1x2xi32>
  func.return %0 : tensor<1x2xi32>
}

// -----

// CHECK-LABEL: constant_tensor_array
func.func @constant_tensor_array() -> !tfr.tensor {

    // CHECK: tf.Const
    // CHECK-SAME: () -> tensor<4xi32>
    %0 = "tf.Const"() {value = dense<[200, 26, 26, 32]> : tensor<4xi32>} : () -> tensor<*xi32>
    // CHECK: tf.Const
    // CHECK-SAME: () -> tensor<4xi32>
    %1 = "tf.Const"() {value = dense<[200, 26, 26, 32]> : tensor<4xi32>} : () -> tensor<*xi32>
    // CHECK: tf.Add
    // CHECK-SAME: (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi32>

    %8 = stablehlo.convert %7 : (tensor<1x2xi32>) -> tensor<1x2xf32>
    %9 = stablehlo.convert %2 : (tensor<2x3xi8>) -> tensor<2x3xf32>
    %10 = stablehlo.dot_general %8, %9, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %11 = stablehlo.convert %3 : (tensor<1x3xi32>) -> tensor<1x3xf32>

  %14 = "mhlo.broadcast_in_dim"(%1) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<i32>) -> tensor<4xi32>
  %15 = mhlo.compare  LT, %13, %14,  SIGNED : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi1>
  %16 = "mhlo.broadcast_in_dim"(%0) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<i32>) -> tensor<4xi32>
  %17 = mhlo.add %13, %16 : tensor<4xi32>

  %cst = "tf.Const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %cst_0 = "tf.Const"() {value = dense<1> : tensor<i32>} : () -> tensor<i32>
  %cst_1 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>
  %cst_2 = "tf.Const"() {value = dense<4> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %0 = "tf.SpaceToBatchND"(%arg0, %cst_1, %cst_2) : (tensor<1x128x3xf32>, tensor<1xi32>, tensor<1x2xi32>) -> tensor<2x68x3xf32>

    // CHECK-NEXT:  %[[RES:.*]] = "tf.SelectV2"(%[[PRED]], %[[SCALED_GRADIENTS]], %[[SELU_GRAD_VALUE]]) : (tensor<4x8xi1>, tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<4x8xf32>
    // CHECK-NEXT:  return %[[RES]] : tensor<4x8xf32>
    %2 = "tf.SeluGrad"(%gradients, %features) : (tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<4x8xf32>
    func.return %2 : tensor<4x8xf32>
}

    %input:2 = tf_executor.island wraps "tf.Const"() {dtype = "tftype$DT_INT32", value = dense<1.0> : tensor<4x6xf32>} : () -> tensor<4x6xf32>
    %split:3 = tf_executor.island wraps "tf.Split"(%dim#0, %input#0) : (tensor<i32>, tensor<4x6xf32>) -> (tensor<2x6xf32>, tensor<2x6xf32>)
    tf_executor.fetch
  }
  func.return

    // CHECK:      %[[ONE:.*]] = "tf.Const"() <{value = dense<1> : tensor<i32>}>
    // CHECK:      %[[RES_READ_VAL:[0-9]*]] = "tf.ReadVariableOp"
    // CHECK-SAME: (tensor<*x!tf_type.resource<tensor<2x8xi32>>>) -> tensor<2x8xi32>
    // CHECK:      "tf.AddV2"(%[[RES_READ_VAL]], %[[ONE]])
    // CHECK-SAME: (tensor<2x8xi32>, tensor<i32>) -> tensor<2x8xi32>
    // CHECK:      "tf.AssignVariableOp"

  // Reduce over spatial dimensions (NCHW)
  %1 = "tf.Const"() {value = dense<[2, 3]> : tensor<2xi32>} : () -> tensor<2xi32>

  // Transpose input: NHWC -> NCHW
  %2 = "tf.Const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
  %3 = "tf.Transpose"(%arg0, %2) : (tensor<?x224x224x3xf32>, tensor<4xi32>) -> tensor<?x3x224x224xf32>

// CHECK-NEXT:      %14 = "tfl.pseudo_const"() <{value = dense<[-1, -1, 0, -1]> : tensor<4xi32>}> : () -> tensor<4xi32>
// CHECK-NEXT:      %15 = "tfl.pseudo_const"() <{value = dense<[-1, -1, -1, 0]> : tensor<4xi32>}> : () -> tensor<4xi32>
// CHECK-NEXT:      %16 = "tfl.pseudo_const"() <{value = dense<1> : tensor<i32>}> : () -> tensor<i32>