diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py
index 99f702ca9e7..597b51fe870 100644
--- a/tensorflow/python/ops/variables.py
+++ b/tensorflow/python/ops/variables.py
@@ -186,6 +186,9 @@ def __init__(self,
     if default_value_dim <=0:
       print("default value dim must larger than 1, the default value dim is set to default 4096.")
       default_value_dim = 4096
+  def __repr__(self):
+    return "[Initializier: {init}".format(init=self.initializer) + \
+           ", default_value_dim: {dim}]".format(dim=self.default_value_dim)
     
 class MultihashOption(object):
   def __init__(self,
@@ -203,6 +206,8 @@ class GlobalStepEvict(object):
   def __init__(self,
                steps_to_live = None):
     self.steps_to_live = steps_to_live
+  def __repr__(self):
+    return "[Eviction type: GlobalStepEvict, steps_to_live: {step}]".format(step=self.steps_to_live)
 
 @tf_export(v1=["L2WeightEvict"])
 class L2WeightEvict(object):
@@ -211,6 +216,8 @@ def __init__(self,
     self.l2_weight_threshold = l2_weight_threshold
     if l2_weight_threshold <= 0 and l2_weight_threshold != -1.0:
       logging.warning("l2_weight_threshold is invalid, l2_weight-based eviction is disabled")
+  def __repr__(self):
+    return "[Eviction type: L2WeightEvict, l2_weight_threshold: {threshold}]".format(threshold=self.l2_weight_threshold)
 
 @tf_export(v1=["CheckpointOption"])
 class CheckpointOption(object):
@@ -250,6 +257,23 @@ def __init__(self,
                                                        config_pb2.StorageType.DRAM_SSDHASH,
                                                        config_pb2.StorageType.DRAM_LEVELDB]:
         raise ValueError("storage_path musnt'be None when storage_type is set")
+  def __repr__(self):
+    name_dict = {}
+    name_dict[None] = "None"
+    name_dict[config_pb2.StorageType.DRAM] = "DRAM"
+    name_dict[config_pb2.StorageType.PMEM_LIBPMEM] = "PMEM_LIBPMEM"
+    name_dict[config_pb2.StorageType.PMEM_MEMKIND] = "PMEM_MEMKIND"
+    name_dict[config_pb2.StorageType.LEVELDB] = "LEVELDB"
+    name_dict[config_pb2.StorageType.SSDHASH] = "SSDHASH"
+    name_dict[config_pb2.StorageType.DRAM_LEVELDB] = "DRAM_LEVELDB"
+    name_dict[config_pb2.StorageType.PMEM_MEMKIND] = "HBM_DRAM"
+    name_dict[config_pb2.StorageType.DRAM_SSDHASH] = "DRAM_SSDHASH"
+    name_dict[config_pb2.StorageType.DRAM_PMEM] = "DRAM_PMEM"
+    name_dict[config_pb2.StorageType.DRAM_PMEM_SSDHASH] = "DRAM_PMEM_SSDHASH"
+    name_dict[config_pb2.StorageType.HBM_DRAM_SSDHASH] = "HBM_DRAM_SSDHASH"
+    return "[Storage type: {type}".format(type=name_dict[self.storage_type]) + \
+           ", storage path: {path}".format(path=self.storage_path) + \
+           ", storage_size: {size}]".format(size=self.storage_size)
 
 @tf_export(v1=["EmbeddingVariableOption"])
 class EmbeddingVariableOption(object):
@@ -267,12 +291,23 @@ def __init__(self,
     self.ckpt = ckpt
     self.filter_strategy = filter_option
     self.storage_option = storage_option
-    self.init = init_option   
+    self.init = init_option
+
+  def __repr__(self):
+    return "ht_type: {type}".format(type=self.ht_type) + \
+            ", ht_partition_num: {num}".format(num=self.ht_partition_num) + \
+            ", evict_option: {evict}".format(evict=self.evict) + \
+            ", filter_strategy: {filter}".format(filter=self.filter_strategy) + \
+            ", storage_option: {storage}".format(storage=self.storage_option) + \
+            ", init_option: {init}".format(init=self.init)
+
     
 @tf_export(v1=["CounterFilter"])
 class CounterFilter(object):
   def __init__(self, filter_freq = 0):
     self.filter_freq = filter_freq
+  def __repr__(self):
+    return "[filter_type: CounterFilter, filter_freq: {freq}]".format(freq=self.filter_freq)
 
 @tf_export(v1=["CBFFilter"])
 class CBFFilter(object):
@@ -294,6 +329,11 @@ def __init__(self,
     self.false_positive_probability = false_positive_probability
     self.counter_type = counter_type
     self.filter_freq = filter_freq
+  def __repr__(self):
+    return "[filter_type: CBFFilter, filter_freq: {freq}".format(freq=self.filter_freq) + \
+          ", max_element_size: {size}".format(size=self.max_element_size) + \
+          ", false_positive_probability: {prob}".format(prob=self.false_positive_probability) + \
+          ", self.counter_type: {type}]".format(type=self.counter_type)
 
 class EmbeddingVariableConfig(object):
   def __init__(self,