Update docstring.
diff --git a/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/bidirectional_lstm_benchmark_test.py b/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/bidirectional_lstm_benchmark_test.py
index 11a1b2b..f18c52c 100644
--- a/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/bidirectional_lstm_benchmark_test.py
+++ b/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/bidirectional_lstm_benchmark_test.py
@@ -25,6 +25,15 @@
 
 class BidirectionalLSTMBenchmark(tf.test.Benchmark):
   """Benchmarks for Bidirectional LSTM using `tf.test.Benchmark`."""
+  # Required Arguments for measure_performance.
+  #   x: Input data, it could be Numpy or load from tfds.
+  #   y: Target data. If `x` is a dataset, generator instance,
+  #      `y` should not be specified.
+  #   loss: Loss function for model.
+  #   optimizer: Optimizer for model.
+  #   Other details can see in `measure_performance()` method of
+  #   benchmark_util.
+
   def __init__(self):
     super(BidirectionalLSTMBenchmark, self).__init__()
     self.max_feature = 20000
@@ -35,7 +44,7 @@
         self.imdb_x, maxlen=self.max_len)
 
   def _build_model(self):
-    """model from https://keras.io/examples/nlp/bidirectional_lstm_imdb/"""
+    """Model from https://keras.io/examples/nlp/bidirectional_lstm_imdb/."""
     inputs = tf.keras.Input(shape=(None,), dtype='int32')
     x = tf.keras.layers.Embedding(self.max_feature, 128)(inputs)
     x = tf.keras.layers.Bidirectional(
@@ -47,18 +56,9 @@
     return model
 
   def benchmark_bidirect_lstm_imdb_bs_128(self):
-    """ Required Arguments for measure_performance.
-
-      x: Input data, it could be Numpy or load from tfds.
-      y: Target data. If `x` is a dataset, generator instance,
-         `y` should not be specified.
-      loss: Loss function for model.
-      optimizer: Optimizer for model.
-      Other details can see in `measure_performance()` method of
-      benchmark_util.
-    """
+    """Measure performance with batch_size=128 and run_iters=3."""
     batch_size = 128
-    run_iters = 1
+    run_iters = 3
     results = benchmark_util.measure_performance(
         self._build_model,
         x=self.imdb_x,
@@ -73,16 +73,7 @@
         iters=run_iters, wall_time=results['wall_time'], extras=results)
 
   def benchmark_bidirect_lstm_imdb_bs_256(self):
-    """ Required Arguments for measure_performance.
-
-      x: Input data, it could be Numpy or load from tfds.
-      y: Target data. If `x` is a dataset, generator instance,
-         `y` should not be specified.
-      loss: Loss function for model.
-      optimizer: Optimizer for model.
-      Other details can see in `measure_performance()` method of
-      benchmark_util.
-    """
+    """Measure performance with batch_size=256 and run_iters=2."""
     batch_size = 256
     run_iters = 2
     results = benchmark_util.measure_performance(
@@ -99,18 +90,9 @@
         iters=run_iters, wall_time=results['wall_time'], extras=results)
 
   def benchmark_bidirect_lstm_imdb_bs_512(self):
-    """ Required Arguments for measure_performance.
-
-      x: Input data, it could be Numpy or load from tfds.
-      y: Target data. If `x` is a dataset, generator instance,
-         `y` should not be specified.
-      loss: Loss function for model.
-      optimizer: Optimizer for model.
-      Other details can see in `measure_performance()` method of
-      benchmark_util.
-    """
+    """Measure performance with batch_size=512 and run_iters=4."""
     batch_size = 512
-    run_iters = 1
+    run_iters = 4
     results = benchmark_util.measure_performance(
         self._build_model,
         x=self.imdb_x,
diff --git a/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/text_classification_transformer_benchmark_test.py b/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/text_classification_transformer_benchmark_test.py
index f070f2d..c589437 100644
--- a/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/text_classification_transformer_benchmark_test.py
+++ b/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/text_classification_transformer_benchmark_test.py
@@ -26,6 +26,15 @@
 class TextWithTransformerBenchmark(tf.test.Benchmark):
   """Benchmarks for Text classification with Transformer
   using `tf.test.Benchmark`."""
+  # Required Arguments for measure_performance.
+  #   x: Input data, it could be Numpy or load from tfds.
+  #   y: Target data. If `x` is a dataset, generator instance,
+  #      `y` should not be specified.
+  #   loss: Loss function for model.
+  #   optimizer: Optimizer for model.
+  #   Other details can see in `measure_performance()` method of
+  #   benchmark_util.
+
   def __init__(self):
     super(TextWithTransformerBenchmark, self).__init__()
     self.max_feature = 20000
@@ -36,7 +45,7 @@
         self.imdb_x, maxlen=self.max_len)
 
   def _build_model(self):
-    """model from https://keras.io/examples/nlp/text_classification_with_transformer/"""
+    """Model from https://keras.io/examples/nlp/text_classification_with_transformer/."""
     embed_dim = 32
     num_heads = 2
     ff_dim = 32
@@ -60,43 +69,8 @@
     model = tf.keras.Model(inputs=inputs, outputs=outputs)
     return model
 
-  def benchmark_text_classification_bs_64(self):
-    """ Required Arguments for measure_performance.
-
-      x: Input data, it could be Numpy or load from tfds.
-      y: Target data. If `x` is a dataset, generator instance,
-         `y` should not be specified.
-      loss: Loss function for model.
-      optimizer: Optimizer for model.
-      Other details can see in `measure_performance()` method of
-      benchmark_util.
-    """
-    batch_size = 64
-    run_iters = 2
-    results = benchmark_util.measure_performance(
-        self._build_model,
-        x=self.imdb_x,
-        y=self.imdb_y,
-        batch_size=batch_size,
-        run_iters=run_iters,
-        optimizer='adam',
-        loss='sparse_categorical_crossentropy',
-        metrics=['accuracy'])
-
-    self.report_benchmark(
-        iters=run_iters, wall_time=results['wall_time'], extras=results)
-
   def benchmark_text_classification_bs_128(self):
-    """ Required Arguments for measure_performance.
-
-      x: Input data, it could be Numpy or load from tfds.
-      y: Target data. If `x` is a dataset, generator instance,
-         `y` should not be specified.
-      loss: Loss function for model.
-      optimizer: Optimizer for model.
-      Other details can see in `measure_performance()` method of
-      benchmark_util.
-    """
+    """Measure performance with batch_size=128 and run_iters=3."""
     batch_size = 128
     run_iters = 3
     results = benchmark_util.measure_performance(
@@ -112,19 +86,27 @@
     self.report_benchmark(
         iters=run_iters, wall_time=results['wall_time'], extras=results)
 
-  def benchmark_text_classification_bs_256(self):
-    """ Required Arguments for measure_performance.
+  def benchmark_text_classification_bs_512(self):
+    """Measure performance with batch_size=512 and run_iters=4."""
+    batch_size = 512
+    run_iters = 4
+    results = benchmark_util.measure_performance(
+        self._build_model,
+        x=self.imdb_x,
+        y=self.imdb_y,
+        batch_size=batch_size,
+        run_iters=run_iters,
+        optimizer='adam',
+        loss='sparse_categorical_crossentropy',
+        metrics=['accuracy'])
 
-      x: Input data, it could be Numpy or load from tfds.
-      y: Target data. If `x` is a dataset, generator instance,
-         `y` should not be specified.
-      loss: Loss function for model.
-      optimizer: Optimizer for model.
-      Other details can see in `measure_performance()` method of
-      benchmark_util.
-    """
+    self.report_benchmark(
+        iters=run_iters, wall_time=results['wall_time'], extras=results)
+
+  def benchmark_text_classification_bs_256(self):
+    """Measure performance with batch_size=256 and run_iters=3."""
     batch_size = 256
-    run_iters = 2
+    run_iters = 3
     results = benchmark_util.measure_performance(
         self._build_model,
         x=self.imdb_x,