const std::vector<float>& expected_values) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Tensor* t = TFE_TensorHandleResolve(handle, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());
  std::unique_ptr<float[]> actual_values(new float[expected_values.size()]);

def ExpandedDynamicShape : NativeCodeCall<"ExpandedDynamicShape($_builder, $0, $1, $2)">;

def : Pat<(MHLO_ConstantOp:$output $value), (TF_ConstOp $value),
          [(TF_Tensor $output)]>;

//===----------------------------------------------------------------------===//
// Binary op patterns.
// Note that these are legalized from chlo.broadcast_* ops, since those are

                              TF_Bool.predicate,
                              TF_TFDialectType.predicate]>,
                          "tf.dtype">;

// Any TensorFlow tensor type
def TF_Tensor : TensorOf<[TF_ElementType]>;

//===----------------------------------------------------------------------===//
// TensorFlow attribute definitions

// CHECK:  "tf.FusedBatchNormV3"(%[[BATCHNORM1_b]], %[[ARG1]], %[[ARG2]], %[[ARG3]], %[[ARG4]])
}


func.func @batchNormWithGlobalNormalization(
    %t:tensor<1x10x10x3xf32>, %m:tensor<3xf32>, %v:tensor<3xf32>, %beta:tensor<3xf32>, %gamma:tensor<3xf32>) -> (tensor<1x10x10x3xf32>) {

    previously `TF_Session` has been renamed to `TF_DeprecatedSession`.
*   C API: Renamed `TF_Port` to `TF_Output`.
*   C API: The caller retains ownership of `TF_Tensor` objects provided to
    `TF_Run`, `TF_SessionRun`, `TF_SetAttrTensor` etc.
*   Renamed `tf.image.per_image_whitening()` to
    `tf.image.per_image_standardization()`