# CHECK-SAME: allow_soft_placement
# CHECK-SAME: control_outputs = ""
# CHECK-SAME: inputs = "args_0,args_1,args_2,args_3"
# CHECK-SAME: outputs = "rets_0,rets_1"
# CHECK:          %[[ISLAND_0:.*]], %[[ISLAND_0_control:.*]] = tf_executor.island wraps "tf.Const"
# CHECK:          %[[ISLAND_1:.*]], %[[ISLAND_1_control:.*]] = tf_executor.island wraps "tf.Identity"(%[[ISLAND_0]])

    // CHECK-NEXT: %[[WHILE:[0-9]*]]:5 = "tf.WhileRegion"(%[[COND]], %[[ARG_1]], %[[ARG_2]], %[[ARG_3]], %[[ARG_4]])
    // CHECK-NEXT:   ^bb0(%[[ARG_5:.*]]: tensor<i1>, %[[ARG_6:.*]]: tensor<i64>, %[[ARG_7:.*]]: tensor<i32>, %[[ARG_8:.*]]: tensor<!tf_type.resource<tensor<64xf32>>>, %[[ARG_9:.*]]: tensor<!tf_type.resource<tensor<128xf32>>>)
    // CHECK-NEXT:     "tf.Yield"(%[[ARG_5]])

    value {
      type: DT_FLOAT
    }
  }
}
node {
  name: "args_0"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_RESOURCE
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
}
node {
  name: "spc1"
  op: "StatefulPartitionedCall"
  input: "input0"
  input: "args_0"
  attr {
    key: "Tin"
    value {
      list {

    func.return %4 : tensor<!tf_type.variant>
  }
  // CHECK-LABEL: __inference_Dataset_flat_map_lambda_190
  func.func private @__inference_Dataset_flat_map_lambda_190(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "__inference_Dataset_flat_map_lambda_19", tf._tf_data_function = true, tf.signature.is_stateful} {

    func.return %4, %6 : tensor<!tf_type.variant>, tensor<!tf_type.variant>
  }
  // CHECK-LABEL: @funcB_renamed
  func.func private @funcB_renamed(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "funcB", tf._tf_data_function = true, tf.signature.is_stateful} {
    %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i64>} : () -> tensor<i64>

                         AbstractContext* ctx) {
  auto CallFn = [fn_name, fn, ctx](TaggedValue args_,
                                   TaggedValue kwargs_) -> TaggedValue {
    std::cout << "Calling " << fn_name << std::endl;
    tensorflow::StatusOr<TaggedValue> v = fn.Execute(ctx, args_);
    return v.value();
  };
  return TaggedValue(CallFn);
}

// Import a module from a saved model.
//

zirain <******@****.***> 1684861711 +0800

	return "arg"
}

func (argsTopic) Activate(context fw.TopicContext) {
	tmpl := assets.ParseTemplate(context.Layout(), "templates/args.html")

	_ = context.HTMLRouter().StrictSlash(true).NewRoute().Path("/").HandlerFunc(func(w http.ResponseWriter, req *http.Request) {
		fw.RenderHTML(w, tmpl, os.Args)
	})

}

// IsEmpty - returns whether reader args is empty or not.
func (args *ReaderArgs) IsEmpty() bool {
	return !args.unmarshaled
}

// UnmarshalXML - decodes XML data.
func (args *ReaderArgs) UnmarshalXML(d *xml.Decoder, start xml.StartElement) (err error) {
	args.FileHeaderInfo = none
	args.RecordDelimiter = defaultRecordDelimiter
	args.FieldDelimiter = defaultFieldDelimiter
	args.QuoteCharacter = defaultQuoteCharacter

Frank Wessels <******@****.***> 1705552466 -0800