From f23830c990f02a92c58c78b5c9f5f39ecae08fca Mon Sep 17 00:00:00 2001
From: Vignesh Kothapalli <vikoth18@in.ibm.com>
Date: Wed, 10 Feb 2021 00:06:35 +0530
Subject: [PATCH] rename op_or_dataset to iterable

---
 .../python/data/benchmarks/benchmark_base.py  | 24 +++++++++----------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/tensorflow/python/data/benchmarks/benchmark_base.py b/tensorflow/python/data/benchmarks/benchmark_base.py
index d1ef9ddf961..e241e8a87d9 100644
--- a/tensorflow/python/data/benchmarks/benchmark_base.py
+++ b/tensorflow/python/data/benchmarks/benchmark_base.py
@@ -31,14 +31,14 @@ from tensorflow.python.platform import test
 class DatasetBenchmarkBase(test.Benchmark):
   """Base class for dataset benchmarks."""
 
-  def _run_eager_benchmark(self, op_or_dataset, iters, warmup):
+  def _run_eager_benchmark(self, iterable, iters, warmup):
     """Benchmark in eager mode.
 
     Runs the op `iters` times. In each iteration, the benchmark measures
     the time it takes to go execute the op.
 
     Args:
-      op_or_dataset: The tf op or tf.data Dataset to benchmark.
+      iterable: The tf op or tf.data Dataset to benchmark.
       iters: Number of times to repeat the timing.
       warmup: If true, warms up the session caches by running an untimed run.
 
@@ -54,10 +54,10 @@ class DatasetBenchmarkBase(test.Benchmark):
 
     for _ in range(iters):
       if warmup:
-        iterator = iter(op_or_dataset)
+        iterator = iter(iterable)
         next(iterator)
 
-      iterator = iter(op_or_dataset)
+      iterator = iter(iterable)
       start = time.time()
       next(iterator)
       end = time.time()
@@ -65,7 +65,7 @@ class DatasetBenchmarkBase(test.Benchmark):
     return np.median(deltas)
 
   def _run_graph_benchmark(self,
-                           op_or_dataset,
+                           iterable,
                            iters,
                            warmup,
                            session_config,
@@ -76,7 +76,7 @@ class DatasetBenchmarkBase(test.Benchmark):
     the time it takes to go execute the op.
 
     Args:
-      op_or_dataset: The tf op or tf.data Dataset to benchmark.
+      iterable: The tf op or tf.data Dataset to benchmark.
       iters: Number of times to repeat the timing.
       warmup: If true, warms up the session caches by running an untimed run.
       session_config: A ConfigProto protocol buffer with configuration options
@@ -95,11 +95,11 @@ class DatasetBenchmarkBase(test.Benchmark):
           "Graph mode benchmarking is not supported in eager mode.")
 
     if is_dataset:
-      iterator = dataset_ops.make_initializable_iterator(dataset)
+      iterator = dataset_ops.make_initializable_iterator(iterable)
       next_element = iterator.get_next()
       op = nest.flatten(next_element)[0].op
     else:
-      op = op_or_dataset
+      op = iterable
 
     for _ in range(iters):
       with session.Session(config=session_config) as sess:
@@ -142,13 +142,13 @@ class DatasetBenchmarkBase(test.Benchmark):
 
     if context.executing_eagerly():
       return self._run_eager_benchmark(
-          op_or_dataset=op,
+          iterable=op,
           iters=iters,
           warmup=warmup
       )
 
     return self._run_graph_benchmark(
-        op_or_dataset=op,
+        iterable=op,
         iters=iters,
         warmup=warmup,
         session_config=session_config,
@@ -200,14 +200,14 @@ class DatasetBenchmarkBase(test.Benchmark):
 
     if context.executing_eagerly():
       median_duration = self._run_eager_benchmark(
-          op_or_dataset=dataset,
+          iterable=dataset,
           iters=iters,
           warmup=warmup
       )
       return median_duration / float(num_elements)
 
     median_duration = self._run_graph_benchmark(
-        op_or_dataset=dataset,
+        iterable=dataset,
         iters=iters,
         warmup=warmup,
         session_config=session_config