fix the summary operator is not work in constant folding scene

The summary operator will be optimized when it return the origin value
in constant folding scene. So I return a None value to avoid this.

mindspore/ops/operations/debug_ops.py

@@ -32,6 +32,13 @@ def _check_summary_param(name, value, class_name):
     validator.check_value_type('value', v_type, [type(mstype.tensor)], class_name)
 
 
+# Note: The return value of the summary operator is not used,
+# so there's nothing special about the return `dtype` or `shape`, any value is ok.
+# The `value` should be set to None, else summary operators may be optimized at compile graph phase,
+# it cause summary operators can not record data in constant folding scene.
+SUMMARY_RETURN_VALUE = {'dtype': mstype.int32, 'shape': [1], 'value': None}
+
+
 class ScalarSummary(PrimitiveWithInfer):
     """
     Output scalar to protocol buffer through scalar summary operator.
@@ -67,7 +74,7 @@ class ScalarSummary(PrimitiveWithInfer):
             raise ValueError(f"For 'value' the type should be scalar, "
                              f"shape should be [] or [1] in {self.__class__.__name__}, but got {v_shape}.")
 
-        return value
+        return SUMMARY_RETURN_VALUE
 
 
 class ImageSummary(PrimitiveWithInfer):
@@ -104,7 +111,7 @@ class ImageSummary(PrimitiveWithInfer):
             raise ValueError(f"For 'value' the dim should be {image_dim} in {self.__class__.__name__},"
                              f" but got {len(v_shape)}.")
 
-        return value
+        return SUMMARY_RETURN_VALUE
 
 
 class TensorSummary(PrimitiveWithInfer):
@@ -142,7 +149,7 @@ class TensorSummary(PrimitiveWithInfer):
             raise ValueError(f"For 'value' the type should be tensor in {self.__class__.__name__}, "
                              f"shape should not be [].")
 
-        return value
+        return SUMMARY_RETURN_VALUE
 
 
 class HistogramSummary(PrimitiveWithInfer):
@@ -180,7 +187,7 @@ class HistogramSummary(PrimitiveWithInfer):
             raise ValueError(f"For 'value' the type should be tensor in {self.__class__.__name__}, "
                              f"shape should not be [].")
 
-        return value
+        return SUMMARY_RETURN_VALUE
 
 
 class InsertGradientOf(PrimitiveWithInfer):