func.func @testBitcast_v9(%arg0: tensor<3x2xi32>) -> tensor<3x2xf32> {
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x2xi32>) -> tensor<3x2xf32>
  func.return %0 : tensor<3x2xf32>
}

// -----

func.func @testBitcast_v10(%arg0: tensor<3x2xi32>) -> tensor<3x4xf32> {
  // expected-error @+1 {{output tensor shape shall be equal to input tensor shape}}
  %0 = "tf.Bitcast"(%arg0) : (tensor<3x2xi32>) -> tensor<3x4xf32>

// `tfl.concatenation`.

func.func @concatenate_float(%arg0: tensor<3x2xf32>, %arg1: tensor<1x2xf32>) -> tensor<4x2xf32> {
  %0 = "stablehlo.concatenate"(%arg0, %arg1) {dimension = 0 : i64} : (tensor<3x2xf32>, tensor<1x2xf32>) -> tensor<4x2xf32>
  return %0 : tensor<4x2xf32>
}
// CHECK-LABEL: concatenate_float
// CHECK-NOT: tfl.concatenation
// CHECK: stablehlo.concatenate

// -----

    %0 = "tf.Const"() {value = dense<2> : tensor<2xi32>} : () -> tensor<2xi32>
    %1 = "tf.Const"() {value = dense<2> : tensor<2x2xi32>} : () -> tensor<2x2xi32>
    %2 = "tf.Identity"(%1) {device = ""} : (tensor<2x2xi32>) -> tensor<2x2xi32>
    // CHECK: SpaceToBatchND{{.*}}-> tensor<4x98x130x128xf32>
    %3 = "tf.SpaceToBatchND"(%arg0, %0, %2) {device = ""} : (tensor<1x192x256x128xf32>, tensor<2xi32>, tensor<2x2xi32>) -> tensor<4x?x?x128xf32>
    func.return

@testLstmWithInvalidInputsRankMatch(%arg0: tensor<1x4xf32>, %arg1: tensor<4x2xf32>, %arg2: tensor<4x2xf32>, %arg3: tensor<4x2xf32>, %arg4: tensor<4x2xf32>, %arg5: tensor<4x4xf32>, %arg6: tensor<4x4xf32>, %arg7: tensor<4x4xf32>, %arg8: tensor<4x4xf32>, %arg9: tensor<4xf32>, %arg10: tensor<4xf32>, %arg11: tensor<4xf32>, %arg12: tensor<1x4xf32>, %arg13: tensor<4xf32>, %arg14: tensor<4xf32>, %arg15: tensor<4xf32>, %arg16: tensor<4x4xf32>, %arg17: tensor<4xf32>, %arg18: tensor<4xf32>, %arg19: tensor<4xf32>,...

  %11 = "tf.Reshape"(%10, %0) {device = ""} : (tensor<4x1x1x2x1xi32>, tensor<2xi32>) -> tensor<4x2xi32>
  %12 = "tf.Transpose"(%11, %6) {device = ""} : (tensor<4x2xi32>, tensor<2xi32>) -> tensor<2x4xi32>
  %13 = "tf.Reshape"(%arg0, %1) {device = ""} : (tensor<1x1x2x1xf32>, tensor<1xi32>) -> tensor<2xf32>
  %14 = "tf.ScatterNd"(%12, %13, %3) {device = ""} : (tensor<2x4xi32>, tensor<2xf32>, tensor<4xi32>) -> tensor<1x2x4x1xf32>

}

func.func @addN(%arg0: tensor<2x3xi32>, %arg1: tensor<2x3xi32>, %arg2: tensor<2x3xi32>) -> tensor<2x3xi32> {
  %0 = "tf.AddN"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
  func.return %0 : tensor<2x3xi32>

// CHECK-LABEL: addN
// CHECK:  "tfl.add_n"(%arg0, %arg1, %arg2) : (tensor<2x3xi32>, tensor<2x3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:  return
}

  %2 = "tf.AddN"(%arg0, %1, %1) : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  %3 = "tf.AddN"(%1, %arg0, %1) : (tensor<2xi32>, tensor<2xi32> , tensor<2xi32>) -> tensor<2xi32>
  %4 = "tf.AddN"(%1, %1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  %5 = "tf.AddN"(%arg0, %1, %0) : (tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %2, %3, %4, %5: tensor<2xi32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>
}

        %5:2 = "tf.C"(%4) : (tensor<2xi32>) -> (tensor<2xi32>, tensor<2xi32>)
        tf_device.return %5#0, %5#1 : tensor<2xi32>, tensor<2xi32>
      }) {num_cores_per_replica = 1, topology =  "", device_assignment =  []} : () -> (tensor<2xi32>, tensor<2xi32>)
      tf_device.return %2#1 : tensor<2xi32>
    }
    func.return %1 : tensor<2xi32>
  }

}

// Returns a RankedTensorType which is similar to `input_type` but replaces the
// dimension size of `dim` with `dim_size`.  For example,
// `SubstituteRankedTensorTypeDimSize(tensor<3x4xi32>, 1, 2)` returns
// `tensor<3x2xi32>`.
static RankedTensorType SubstituteRankedTensorTypeDimSize(
    RankedTensorType input_type, int64_t dim, int64_t dim_size) {
  auto shape = input_type.getShape().vec();
  shape[dim] = dim_size;