func.return %0 : tensor<4xi32>
}

func.func @i64() -> tensor<4xi64> {
  // CHECK-LABEL: @i64
  // CHECK: value = dense<[1, 2, 3, 72623859790382856]> : tensor<4xi64>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 72623859790382856]> : tensor<4xi64> } : () -> tensor<4xi64>
  func.return %0 : tensor<4xi64>
}

func.func @string() -> tensor<2x2x!tf_type.string> {
  // CHECK-LABEL: @string

    %cst = arith.constant dense<[2, 100]> : tensor<2xi64>
    %0 = "tfl.concatenation"(%arg0, %arg1) {axis = 0 : i32, fused_activation_function = "NONE"} : (tensor<100xf32>, tensor<100xf32>) -> tensor<200xf32>
    %1 = "tfl.reshape"(%0, %cst) : (tensor<200xf32>, tensor<2xi64>) -> tensor<2x100xf32>
    func.return %1 : tensor<2x100xf32>
  }

}

// CHECK-LABEL: func @testShapeOp
// CHECK-SAME: ({{%.+}}: tensor<*xi64> {tf.device = "/job:localhost/replica:0/task:0/device:TPU:0"})
// CHECK-SAME: -> (tensor<?xi64> {tf.device = "/job:localhost/replica:0/task:0/device:TPU:0"})
func.func @testShapeOp(%arg0: tensor<*xi64> {tf.device = "/job:localhost/replica:0/task:0/device:TPU:0"}) -> tensor<?xi64> {
  %0 = tf_executor.graph {
    // CHECK:      tf.Shape

  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  %2 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

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

// CHECK-LABEL: @ShapeOpI64
func.func @ShapeOpI64(%arg0 : tensor<576x72xf32>) -> tensor<2xi64> {
  %0 = "tfl.shape"(%arg0) : (tensor<576x72xf32>) -> tensor<2xi64>
  func.return %0 : tensor<2xi64>
  // CHECK: %[[CST:.*]] = arith.constant dense<[576, 72]> : tensor<2xi64>
  // CHECK:  return %[[CST]]
}

// CHECK-LABEL: @ConstFoldStridedSlice

// MIN-MAX-CHECK-NEXT:  [[lhs:%.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%arg0) <{calibration_method = 1 : i32, id = "composite_conv2d_with_relu6_fn_arg_0_calibration_method_1", max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32, num_bins = 0 : i32}> : (tensor<*xf32>) -> (tensor<*xf32>, tensor<f32>, tensor<f32>, tensor<0xi64>)

  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  %2 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>
  func.return %1, %2 : tensor<2x1xf32>, tensor<2x1xf32>

    %dim = "tf.Const"() { value = dense<0> : tensor<1xi64> } : () -> tensor<1xi64>

    // Check and include zero in the range so that zero value can be correctly
    // represented.
    %input_1d = "tf.Reshape"(%input, %shape) : (tensor<*xf32>, tensor<1xi32>) -> tensor<?xf32>
    %r_max_without_zero = "tf.Max"(%input_1d, %dim) { keep_dims = true }: (tensor<?xf32>, tensor<1xi64>) -> tensor<1xf32>

    %outputs_11, %control_12 = tf_executor.island wraps "tf.Const"() {value = dense<[0, 1, 2]> : tensor<3xi64>} : () -> tensor<3xi64>

  %1 = "tfr.constant_tensor"(%0) : (!tfr.attr) -> !tfr.tensor
  func.return %1 : !tfr.tensor

// CHECK-NEXT: %[[RES:.*]] = "tf.Const"() <{value = dense<[1, -1, 3]> : tensor<3xi64>}> : () -> tensor<3xi64>
// CHECK-NEXT: "tfr.cast"(%[[RES]]) : (tensor<3xi64>) -> !tfr.tensor
// CHECK-NEXT: return
}

// -----

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