= "args_7"}, %arg8: tensor<?x?xf32> {tf._user_specified_name = "args_8"}, %arg9: tensor<?x300xi32> {tf._user_specified_name = "args_9"}, %arg10: tensor<?x300xi32> {tf._user_specified_name = "args_10"}, %arg11: tensor<?x300xf32> {tf._user_specified_name = "args_11"}, %arg12: tensor<?x300xf32> {tf._user_specified_name = "args_12"}, %arg13: tensor<?x!tf_type.string> {tf._user_specified_name = "args_13"}, %arg14: tensor<?x!tf_type.string> {tf._user_specified_name = "args_14"}, %arg15: tensor<!tf_ty...

x f32>, %arg4: tensor<? x ? x f32>, %arg5: tensor<? x ? x f32>, %arg6: tensor<? x ? x f32>, %arg7: tensor<? x ? x f32>, %arg8: tensor<? x ? x f32>, %arg9: tensor<? x f32>, %arg10: tensor<? x f32>, %arg11: tensor<? x f32>, %arg12: tensor<? x f32>, %arg13: tensor<? x f32>, %arg14: tensor<? x f32>, %arg15: tensor<? x f32>, %arg16: none, %arg17: none, %arg18: tensor<? x ? x f32>, %arg19: tensor<? x ? xf32>, %arg20: tensor<? x f32>, %arg21: tensor<? x f32>, %arg22: tensor<? x f32>, %arg23: tensor<? x f32>)...

      func.return
    }
    ```

    with the following reduction function:

    ```mlir
    func.func private @__reduce_func_1(%arg0: tensor<i64> {tf._user_specified_name = "args_0"},
      %arg1: tensor<32xf32> {tf._user_specified_name = "args_1"}) -> (tensor<i64>)
      attributes {tf._tf_data_function = true, tf.signature.is_stateful} {
        %0 = "tf.JustPretend"(%arg1) : (tensor<32xf32>) -> (tensor<i64>)

// Hoist coefficient-wise unary operation out of the Concat op:
//
//   %0 = "tf.Log1p"(%arg_0)
//   %1 = "tf.Log1p"(%arg_1)
//   ...
//   %n = "tf.Log1p"(%arg_n)
//   %m = "tf.ConcatV2"(%0, %1, ..., %n, %axis)
//
// Rewrite it to:
//
//   %0 = "tf.ConcatV2"(%arg_0, %arg_1, ..., %arg_n, %axis)
//   %1 = "tf.Log1p"(%0)
class HoistCwiseUnaryOutOfConcat : public OpRewritePattern<TF::ConcatV2Op> {
 public: