diff --git a/tensorflow/contrib/learn/python/learn/dataframe/dataframe.py b/tensorflow/contrib/learn/python/learn/dataframe/dataframe.py
index 31093b9937a..6e03f086425 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/dataframe.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/dataframe.py
@@ -117,10 +117,11 @@ class DataFrame(object):
       value = [value]
     self.assign(**dict(zip(key, value)))
 
-  def build(self):
+  def build(self, **kwargs):
     # We do not allow passing a cache here, because that would encourage
     # working around the rule that DataFrames cannot be expected to be
     # synced with each other (e.g., they shuffle independently).
     cache = {}
-    tensors = {name: c.build(cache) for name, c in self._columns.items()}
+    tensors = {name: c.build(cache, **kwargs)
+               for name, c in self._columns.items()}
     return tensors
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/estimator_utils.py b/tensorflow/contrib/learn/python/learn/dataframe/estimator_utils.py
index bff0c4e4af0..313ae41cfe8 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/estimator_utils.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/estimator_utils.py
@@ -91,7 +91,8 @@ def _build_alternate_universe(
 def to_feature_columns_and_input_fn(dataframe,
                                     base_input_keys_with_defaults,
                                     feature_keys,
-                                    target_keys=None):
+                                    target_keys=None,
+                                    **kwargs):
   """Build a list of FeatureColumns and an input_fn for use with Estimator.
 
   Args:
@@ -103,6 +104,7 @@ def to_feature_columns_and_input_fn(dataframe,
       These may include base features and/or derived features.
     target_keys: the names of columns to be used as targets.  None is
       acceptable for unsupervised learning.
+    **kwargs: Additional keyword arguments, unused here.
 
   Returns:
     A tuple of two elements:
@@ -155,10 +157,11 @@ def to_feature_columns_and_input_fn(dataframe,
 
   # Build an input_fn suitable for use with Estimator.
   def input_fn():
+    """An input_fn() for feeding the given set of DataFrameColumns."""
     # It's important to build all the tensors together in one DataFrame.
     # If we did df.select() for both key sets and then build those, the two
     # resulting DataFrames would be shuffled independently.
-    tensors = limited_dataframe.build()
+    tensors = limited_dataframe.build(**kwargs)
 
     base_input_features = {key: tensors[key] for key in base_input_keys}
     targets = {key: tensors[key] for key in target_keys}
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/series.py b/tensorflow/contrib/learn/python/learn/dataframe/series.py
index 12daa7d7cb8..5893db3aad2 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/series.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/series.py
@@ -98,7 +98,7 @@ class Series(object):
       return transform_cls
     return register
 
-  def build(self, cache):
+  def build(self, cache, **kwargs):
     """Returns a Tensor."""
     raise NotImplementedError()
 
@@ -122,7 +122,7 @@ class PredefinedSeries(Series):
   def required_base_features(self):
     return {self.name: self.feature_spec}
 
-  def build(self, cache):
+  def build(self, cache, **kwargs):
     try:
       return cache[self.name]
     except KeyError:
@@ -171,10 +171,11 @@ class TransformedSeries(Series):
       result.update(s.required_base_features)
     return result
 
-  def build(self, cache=None):
+  def build(self, cache=None, **kwargs):
     if cache is None:
       cache = {}
-    all_outputs = self._transform.build_transitive(self._input_series, cache)
+    all_outputs = self._transform.build_transitive(
+        self._input_series, cache, **kwargs)
     return getattr(all_outputs, self._output_name)
 
   def __repr__(self):
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py b/tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py
index 45df3ac16d5..4b6091dc16c 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/tensorflow_dataframe.py
@@ -83,7 +83,8 @@ class TensorFlowDataFrame(df.DataFrame):
           graph=None,
           session=None,
           start_queues=True,
-          initialize_variables=True):
+          initialize_variables=True,
+          **kwargs):
     """Builds and runs the columns of the `DataFrame` and yields batches.
 
     This is a generator that yields a dictionary mapping column names to
@@ -97,6 +98,7 @@ class TensorFlowDataFrame(df.DataFrame):
       start_queues: if true, queues will be started before running and halted
         after producting `n` batches.
       initialize_variables: if true, variables will be initialized.
+      **kwargs: Additional keyword arguments, unused here.
 
     Yields:
       A dictionary, mapping column names to the values resulting from running
@@ -107,7 +109,7 @@ class TensorFlowDataFrame(df.DataFrame):
     with graph.as_default():
       if session is None:
         session = sess.Session()
-      self_built = self.build()
+      self_built = self.build(**kwargs)
       keys = list(self_built.keys())
       cols = list(self_built.values())
       if initialize_variables:
@@ -208,7 +210,7 @@ class TensorFlowDataFrame(df.DataFrame):
 
   @classmethod
   def _from_csv_base(cls, filepatterns, get_default_values, has_header,
-                     column_names, num_epochs, num_threads, enqueue_size,
+                     column_names, num_threads, enqueue_size,
                      batch_size, queue_capacity, min_after_dequeue, shuffle,
                      seed):
     """Create a `DataFrame` from CSV files.
@@ -223,9 +225,6 @@ class TensorFlowDataFrame(df.DataFrame):
         each column, given the column names.
       has_header: whether or not the CSV files have headers.
       column_names: a list of names for the columns in the CSV files.
-      num_epochs: the number of times that the reader should loop through all
-        the file names. If set to `None`, then the reader will continue
-        indefinitely.
       num_threads: the number of readers that will work in parallel.
       enqueue_size: block size for each read operation.
       batch_size: desired batch size.
@@ -265,7 +264,6 @@ class TensorFlowDataFrame(df.DataFrame):
         reader_kwargs=reader_kwargs,
         enqueue_size=enqueue_size,
         batch_size=batch_size,
-        num_epochs=num_epochs,
         queue_capacity=queue_capacity,
         shuffle=shuffle,
         min_after_dequeue=min_after_dequeue,
@@ -287,7 +285,6 @@ class TensorFlowDataFrame(df.DataFrame):
                default_values,
                has_header=True,
                column_names=None,
-               num_epochs=None,
                num_threads=1,
                enqueue_size=None,
                batch_size=32,
@@ -306,9 +303,6 @@ class TensorFlowDataFrame(df.DataFrame):
       default_values: a list of default values for each column.
       has_header: whether or not the CSV files have headers.
       column_names: a list of names for the columns in the CSV files.
-      num_epochs: the number of times that the reader should loop through all
-        the file names. If set to `None`, then the reader will continue
-        indefinitely.
       num_threads: the number of readers that will work in parallel.
       enqueue_size: block size for each read operation.
       batch_size: desired batch size.
@@ -332,7 +326,7 @@ class TensorFlowDataFrame(df.DataFrame):
       return default_values
 
     return cls._from_csv_base(filepatterns, get_default_values, has_header,
-                              column_names, num_epochs, num_threads,
+                              column_names, num_threads,
                               enqueue_size, batch_size, queue_capacity,
                               min_after_dequeue, shuffle, seed)
 
@@ -342,7 +336,6 @@ class TensorFlowDataFrame(df.DataFrame):
                                  feature_spec,
                                  has_header=True,
                                  column_names=None,
-                                 num_epochs=None,
                                  num_threads=1,
                                  enqueue_size=None,
                                  batch_size=32,
@@ -362,9 +355,6 @@ class TensorFlowDataFrame(df.DataFrame):
           `VarLenFeature`.
       has_header: whether or not the CSV files have headers.
       column_names: a list of names for the columns in the CSV files.
-      num_epochs: the number of times that the reader should loop through all
-        the file names. If set to `None`, then the reader will continue
-        indefinitely.
       num_threads: the number of readers that will work in parallel.
       enqueue_size: block size for each read operation.
       batch_size: desired batch size.
@@ -387,7 +377,7 @@ class TensorFlowDataFrame(df.DataFrame):
       return [_get_default_value(feature_spec[name]) for name in column_names]
 
     dataframe = cls._from_csv_base(filepatterns, get_default_values, has_header,
-                                   column_names, num_epochs, num_threads,
+                                   column_names, num_threads,
                                    enqueue_size, batch_size, queue_capacity,
                                    min_after_dequeue, shuffle, seed)
 
@@ -405,7 +395,6 @@ class TensorFlowDataFrame(df.DataFrame):
                     filepatterns,
                     features,
                     reader_cls=io_ops.TFRecordReader,
-                    num_epochs=None,
                     num_threads=1,
                     enqueue_size=None,
                     batch_size=32,
@@ -421,9 +410,6 @@ class TensorFlowDataFrame(df.DataFrame):
         `FixedLenFeature`.
       reader_cls: a subclass of `tensorflow.ReaderBase` that will be used to
         read the `Example`s.
-      num_epochs: the number of times that the reader should loop through all
-        the file names. If set to `None`, then the reader will continue
-        indefinitely.
       num_threads: the number of readers that will work in parallel.
       enqueue_size: block size for each read operation.
       batch_size: desired batch size.
@@ -454,7 +440,6 @@ class TensorFlowDataFrame(df.DataFrame):
         filenames,
         enqueue_size=enqueue_size,
         batch_size=batch_size,
-        num_epochs=num_epochs,
         queue_capacity=queue_capacity,
         shuffle=shuffle,
         min_after_dequeue=min_after_dequeue,
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transform.py b/tensorflow/contrib/learn/python/learn/dataframe/transform.py
index 745d556f929..bbb97d2f290 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transform.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transform.py
@@ -223,13 +223,14 @@ class Transform(object):
     # pylint: disable=not-callable
     return self.return_type(*output_series)
 
-  def build_transitive(self, input_series, cache=None):
+  def build_transitive(self, input_series, cache=None, **kwargs):
     """Apply this `Transform` to the provided `Series`, producing 'Tensor's.
 
     Args:
       input_series: None, a `Series`, or a list of input `Series`, acting as
          positional arguments.
       cache: a dict from Series reprs to Tensors.
+      **kwargs: Additional keyword arguments, unused here.
 
     Returns:
       A namedtuple of the output Tensors.
@@ -244,7 +245,7 @@ class Transform(object):
     if len(input_series) != self.input_valency:
       raise ValueError("Expected %s input Series but received %s." %
                        (self.input_valency, len(input_series)))
-    input_tensors = [series.build(cache) for series in input_series]
+    input_tensors = [series.build(cache, **kwargs) for series in input_series]
 
     # Note we cache each output individually, not just the entire output
     # tuple.  This allows using the graph as the cache, since it can sensibly
@@ -254,7 +255,7 @@ class Transform(object):
     output_tensors = [cache.get(output_repr) for output_repr in output_reprs]
 
     if None in output_tensors:
-      result = self._apply_transform(input_tensors)
+      result = self._apply_transform(input_tensors, **kwargs)
       for output_name, output_repr in zip(self.output_names, output_reprs):
         cache[output_repr] = getattr(result, output_name)
     else:
@@ -264,12 +265,13 @@ class Transform(object):
     return result
 
   @abstractmethod
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     """Applies the transformation to the `transform_input`.
 
     Args:
-        input_tensors: a list of Tensors representing the input to
+      input_tensors: a list of Tensors representing the input to
         the Transform.
+      **kwargs: Additional keyword arguments, unused here.
 
     Returns:
         A namedtuple of Tensors representing the transformed output.
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/batch.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/batch.py
index 352a028ee33..cf1585634ca 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/batch.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/batch.py
@@ -72,7 +72,7 @@ class Batch(AbstractBatchTransform):
   def name(self):
     return "Batch"
 
-  def _apply_transform(self, transform_input):
+  def _apply_transform(self, transform_input, **kwargs):
     batched = input_ops.batch(transform_input,
                               batch_size=self.batch_size,
                               num_threads=self.num_threads,
@@ -121,7 +121,7 @@ class ShuffleBatch(AbstractBatchTransform):
   def seed(self):
     return self._seed
 
-  def _apply_transform(self, transform_input):
+  def _apply_transform(self, transform_input, **kwargs):
     batched = input_ops.shuffle_batch(transform_input,
                                       batch_size=self.batch_size,
                                       capacity=self.queue_capacity,
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/binary_transforms.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/binary_transforms.py
index 7d46fb6d05e..78a21250c9c 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/binary_transforms.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/binary_transforms.py
@@ -1,4 +1,4 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -53,7 +53,7 @@ class SeriesBinaryTransform(transform.Transform):
   def _output_names(self):
     return "output",
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     # TODO(jamieas): consider supporting sparse inputs.
     if isinstance(input_tensors[0], ops.SparseTensor) or isinstance(
         input_tensors[1], ops.SparseTensor):
@@ -87,7 +87,7 @@ class ScalarBinaryTransform(transform.Transform):
   def _output_names(self):
     return "output",
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     input_tensor = input_tensors[0]
     if isinstance(input_tensor, ops.SparseTensor):
       result = ops.SparseTensor(input_tensor.indices,
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
index f572cf137f7..758de866e21 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/boolean_mask.py
@@ -77,12 +77,13 @@ class BooleanMask(transform.Transform):
   def _output_names(self):
     return "output",
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     """Applies the transformation to the `transform_input`.
 
     Args:
-        input_tensors: a list of Tensors representing the input to
+      input_tensors: a list of Tensors representing the input to
         the Transform.
+      **kwargs: Additional keyword arguments, unused here.
 
     Returns:
         A namedtuple of Tensors representing the transformed output.
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/csv_parser.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/csv_parser.py
index caa83f5a966..d78b5652d6e 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/csv_parser.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/csv_parser.py
@@ -58,7 +58,7 @@ class CSVParser(transform.Transform):
   def default_values(self):
     return self._default_values
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     default_consts = [constant_op.constant(d, shape=[1])
                       for d in self._default_values]
     parsed_values = parsing_ops.decode_csv(input_tensors[0],
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/densify.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/densify.py
index 2f389153178..0f0c1a08911 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/densify.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/densify.py
@@ -47,12 +47,13 @@ class Densify(transform.Transform):
   def _output_names(self):
     return "output",
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     """Applies the transformation to the `transform_input`.
 
     Args:
-        input_tensors: a list of Tensors representing the input to
+      input_tensors: a list of Tensors representing the input to
         the Transform.
+      **kwargs: Additional keyword arguments, unused here.
 
     Returns:
         A namedtuple of Tensors representing the transformed output.
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/difference.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/difference.py
index d4e6c10094b..b585fceeb63 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/difference.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/difference.py
@@ -1,4 +1,4 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -50,7 +50,7 @@ class Difference(transform.Transform):
   def _output_names(self):
     return "output",
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     pair_sparsity = (isinstance(input_tensors[0], ops.SparseTensor),
                      isinstance(input_tensors[1], ops.SparseTensor))
 
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/example_parser.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/example_parser.py
index e22ef740ed9..c2c5e0cbed5 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/example_parser.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/example_parser.py
@@ -61,7 +61,7 @@ class ExampleParser(transform.Transform):
   def feature_definitions(self):
     return self._ordered_features
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     parsed_values = parsing_ops.parse_example(input_tensors[0],
                                               features=self._ordered_features)
     # pylint: disable=not-callable
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
index 97453c30325..d96d53468a5 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/in_memory_source.py
@@ -89,7 +89,7 @@ class BaseInMemorySource(transform.Transform):
   def input_valency(self):
     return 0
 
-  def _apply_transform(self, transform_input):
+  def _apply_transform(self, transform_input, **kwargs):
     queue = feeding_functions.enqueue_data(self.data,
                                            self.queue_capacity,
                                            self.shuffle,
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py
index 23556c40657..ddb2d321d1c 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/reader_source.py
@@ -32,7 +32,6 @@ class ReaderSource(transform.Transform):
                reader_kwargs=None,
                enqueue_size=None,
                batch_size=1,
-               num_epochs=None,
                queue_capacity=None,
                shuffle=False,
                min_after_dequeue=None,
@@ -49,9 +48,6 @@ class ReaderSource(transform.Transform):
         is constructed.
       enqueue_size: block size for each read operation.
       batch_size: The desired batch size of output. Defaults to 1.
-      num_epochs: the number of times that the reader should loop through all
-        the file names. If set to `None`, then the reader will continue
-        indefinitely.
       queue_capacity: Capacity of the queue. Defaults to 10 * `batch_size`.
       shuffle: Whether records will be shuffled before returning. Defaults to
         false.
@@ -73,7 +69,6 @@ class ReaderSource(transform.Transform):
     self._batch_size = batch_size
     self._queue_capacity = (batch_size * 10 if queue_capacity is None else
                             queue_capacity)
-    self._num_epochs = num_epochs
     self._shuffle = shuffle
     self._min_after_dequeue = int(self.queue_capacity / 4 if min_after_dequeue
                                   is None else min_after_dequeue)
@@ -100,10 +95,6 @@ class ReaderSource(transform.Transform):
   def batch_size(self):
     return self._batch_size
 
-  @transform.parameter
-  def num_epochs(self):
-    return self._num_epochs
-
   @transform.parameter
   def queue_capacity(self):
     return self._queue_capacity
@@ -136,11 +127,12 @@ class ReaderSource(transform.Transform):
   def _output_names(self):
     return ("index", "value")
 
-  def _apply_transform(self, transform_input):
-    filename_queue = input_ops.string_input_producer(self.work_units,
-                                                     num_epochs=self.num_epochs,
-                                                     shuffle=self.shuffle,
-                                                     seed=self.seed)
+  def _apply_transform(self, transform_input, **kwargs):
+    filename_queue = input_ops.string_input_producer(
+        self.work_units,
+        num_epochs=kwargs.get("num_epochs"),
+        shuffle=self.shuffle,
+        seed=self.seed)
     reader_ops = []
     for _ in range(self.num_threads):
       reader = self._reader_cls(**self._reader_kwargs)
@@ -174,7 +166,6 @@ def TextFileSource(file_names,
                    reader_kwargs=None,
                    enqueue_size=1,
                    batch_size=1,
-                   num_epochs=None,
                    queue_capacity=None,
                    shuffle=False,
                    min_after_dequeue=None,
@@ -185,7 +176,6 @@ def TextFileSource(file_names,
                       reader_kwargs=reader_kwargs,
                       enqueue_size=enqueue_size,
                       batch_size=batch_size,
-                      num_epochs=num_epochs,
                       queue_capacity=queue_capacity,
                       shuffle=shuffle,
                       min_after_dequeue=min_after_dequeue,
@@ -197,7 +187,6 @@ def TFRecordSource(file_names,
                    reader_kwargs=None,
                    enqueue_size=1,
                    batch_size=1,
-                   num_epochs=None,
                    queue_capacity=None,
                    shuffle=False,
                    min_after_dequeue=None,
@@ -208,7 +197,6 @@ def TFRecordSource(file_names,
                       reader_kwargs=reader_kwargs,
                       enqueue_size=enqueue_size,
                       batch_size=batch_size,
-                      num_epochs=num_epochs,
                       queue_capacity=queue_capacity,
                       shuffle=shuffle,
                       min_after_dequeue=min_after_dequeue,
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/sparsify.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/sparsify.py
index 552012ea330..f3447c5d940 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/sparsify.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/sparsify.py
@@ -52,12 +52,13 @@ class Sparsify(transform.Transform):
   def _output_names(self):
     return "output",
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     """Applies the transformation to the `transform_input`.
 
     Args:
-        input_tensors: a list of Tensors representing the input to
+      input_tensors: a list of Tensors representing the input to
         the Transform.
+      **kwargs: Additional keyword arguments, unused here.
 
     Returns:
         A namedtuple of Tensors representing the transformed output.
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/sum.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/sum.py
index 6b04166e09c..878b08f4b0a 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/sum.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/sum.py
@@ -1,4 +1,4 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -44,7 +44,7 @@ class Sum(transform.Transform):
   def _output_names(self):
     return "output",
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     pair_sparsity = (isinstance(input_tensors[0], ops.SparseTensor),
                      isinstance(input_tensors[1], ops.SparseTensor))
 
diff --git a/tensorflow/contrib/learn/python/learn/dataframe/transforms/unary_transforms.py b/tensorflow/contrib/learn/python/learn/dataframe/transforms/unary_transforms.py
index 3fd8c2a6a90..058ce1ed248 100644
--- a/tensorflow/contrib/learn/python/learn/dataframe/transforms/unary_transforms.py
+++ b/tensorflow/contrib/learn/python/learn/dataframe/transforms/unary_transforms.py
@@ -1,4 +1,4 @@
-# Copyright 2016 Google Inc. All Rights Reserved.
+# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -78,7 +78,7 @@ def register_unary_op(registered_name, operation):
   def _output_names(self):
     return "output"
 
-  def _apply_transform(self, input_tensors):
+  def _apply_transform(self, input_tensors, **kwargs):
     input_tensor = input_tensors[0]
     if isinstance(input_tensor, ops.SparseTensor):
       result = ops.SparseTensor(input_tensor.indices,
diff --git a/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py b/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py
index 14e283cb791..7e233f33849 100644
--- a/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py
+++ b/tensorflow/contrib/learn/python/learn/tests/dataframe/tensorflow_dataframe_test.py
@@ -208,10 +208,9 @@ class TensorFlowDataFrameTestCase(tf.test.TestCase):
     tensorflow_df = df.TensorFlowDataFrame.from_csv(
         [data_path],
         batch_size=batch_size,
-        num_epochs=num_epochs,
         shuffle=False,
         default_values=default_values)
-    actual_num_batches = len(list(tensorflow_df.run()))
+    actual_num_batches = len(list(tensorflow_df.run(num_epochs=num_epochs)))
     self.assertEqual(expected_num_batches, actual_num_batches)
 
   def testFromCSVWithFeatureSpec(self):