%0:3 = "tf.Split"(%arg0, %arg1) : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
  func.return %0#0 : tensor<1x4x3xf32>

  // CHECK-LABEL: split
  // CHECK: "tfl.split"(%arg0, %arg1) <{num_splits = 3 : i32}> : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
}

  func.func @operand_as_shape(%18: tensor<i32>, %39: tensor<1x4x4x32xf32>) -> () {
    %cst_5 = arith.constant dense<512> : tensor<i32>
    %19 = "tf.Pack"(%18, %cst_5) {N = 2 : i64, T = i32, axis = 0 : i64, device = ""} : (tensor<i32>, tensor<i32>) -> tensor<2xi32>
    // CHECK: -> tensor<1x512xf32>
    %40 = "tf.Reshape"(%39, %19) {T = f32, Tshape = i32, device = ""} : (tensor<1x4x4x32xf32>, tensor<2xi32>) -> tensor<?x?xf32>
   func.return
  }

// does not alter the last dimension and it restores the batch dimensions
// collapsed by the FullyConnected op due to `keep_num_dims=false`. For example,
//
//   // %input: tensor<4x16x32xf32>
//   %fc = tfl.fully_connected(%input, %filter, %bias)
//           {keep_num_dims = false, weights_format = "DEFAULT"}
//   %shape = arith.constant dense<[4, 16, 32]> : tensor<3xi32>

}

// -----

func.func @testPReluInvalidBroadcast(%arg0: tensor<15x14x2x14xf32>, %arg1: tensor<1x1x3xf32>) -> tensor<15x14x2x14xf32> {
  // expected-error @+1 {{'tfl.prelu' op operands don't have broadcast-compatible shapes}}
  %0 = "tfl.prelu"(%arg0, %arg1) : (tensor<15x14x2x14xf32>, tensor<1x1x3xf32>) -> tensor<15x14x2x14xf32>
  func.return %0 : tensor<15x14x2x14xf32>
}

// -----