// CHECK:           [[VAL_12:%.*]]:4 = "tf.SplitV"([[VAL_7]], [[VAL_10]], [[VAL_11]]) : (tensor<40x8xf32>, tensor<4xi32>, tensor<i32>) -> (tensor<10x8xf32>, tensor<10x8xf32>, tensor<10x8xf32>, tensor<10x8xf32>)
// CHECK-DAG:       [[VAL_13:%.*]] = "tf.Const"() <{value = dense<10> : tensor<4xi32>}> : () -> tensor<4xi32>

func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x8xf32>) -> tensor<*xf32> {
  %0 = "tf.Pack"(%arg0, %arg1) {axis = 1 : i64} : (tensor<4x8xf32>, tensor<4x8xf32>) -> tensor<*xf32>
  func.return %0 : tensor<*xf32>
}


// -----

func.func @testPack(%arg0: tensor<4x8xf32>, %arg1: tensor<4x2xf32>) -> tensor<*xf32> {

  func.func @set_dynamic_dimension_size_static_dim(%input: tensor<4x5xf32>, %size: tensor<i32>) -> tensor<*xf32> {
    %dimension = "tf.Const"() { value = dense<0> : tensor<i32> } : () -> tensor<i32>

    // CHECK: (tensor<4x5xf32>, tensor<i32>, tensor<i32>) -> tensor<?x5xf32>
    %0 = "tf.XlaSetDynamicDimensionSize"(%input, %dimension, %size) : (tensor<4x5xf32>, tensor<i32>, tensor<i32>) -> tensor<*xf32>
    func.return %0 : tensor<*xf32>

  %0 = "tf.MatMul"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", device = "/device:CPU:0", name = "MatMul", transpose_a = false, transpose_b = true} :
(tensor<40x37xf32>, tensor<40x37xf32>) -> tensor<40x40xf32>
  func.return %0 : tensor<40x40xf32>
// CHECK-LABEL: matmul_transposed_b
// CHECK: %[[CST:.*]] = "tfl.no_value"() <{value}> : () -> none

  %3 = "tf.AddV2"(%arg0, %0): (tensor<4x4xf32>, tensor<1xf32>) -> tensor<4x4xf32>
  %4 = "tf.Log"(%3) {device = "/job:localhost/replica:0/task:0/device:GPU:0"}: (tensor<4x4xf32>) -> tensor<4x4xf32>

  // CHECK: %[[ADD1:.*]] = "tf.AddV2"
  // CHECK: %[[LOG1:.*]] = "tf.Log"(%[[ADD1]])
  %5 = "tf.AddV2"(%4, %1): (tensor<4x4xf32>, tensor<1xf32>) -> tensor<4x4xf32>

@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>,...

    } else {
      // Recurse on the subtypes in the variant/resource. Basically if the input
      // were:
      //   tensor<!tf_type.variant<tensor<?x8xf32>>>
      // and:
      //   tensor<!tf_type.variant<tensor<10x8xf32>>>
      // we'll try here to refine tensor<?x8xf32> with tensor<10x8xf32>.
      auto refined_subtype = mlir::cast<TensorType>(
          TypeMeet(lhs_element_type_with_subtype.GetSubtypes().front(),

    data and model parallelism in an easier manner.

    For example, the following:

    ```mlir
    !rtype = type tensor<!tf_type.resource<tensor<10x3xf32>>>
    func @data_and_model_parallelism(%arg0: !rtype, %arg1: !rtype, %arg2: !rtype, %arg3: !rtype) -> !rtype {