chore: import upstream snapshot with attribution

tensorflow/python/eager/benchmarks/resnet50/README.md

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+Image classification using the ResNet50 model described in
+[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385).
+
+Contents:
+
+- `resnet50.py`: Model definition
+- `resnet50_test.py`: Sanity unittests and benchmarks for using the model with
+  eager execution enabled.
+- `resnet50_graph_test.py`: Sanity unittests and benchmarks when using the same
+  model code to construct a TensorFlow graph.
+
+# Benchmarks
+
+Using a synthetic data, run:
+
+```
+# Using eager execution
+python resnet50_test.py --benchmark_filter=.
+
+# Using graph execution
+python resnet50_graph_test.py --benchmark_filter=.
+```
+
+The above uses the model definition included with the TensorFlow pip
+package. To build (and run benchmarks) from source:
+
+```
+# Using eager execution
+bazel run -c opt --config=cuda :resnet50_test -- --benchmark_filter=.
+
+# Using graph execution
+bazel run -c opt --config=cuda :resnet50_graph_test -- --benchmark_filter=.
+```
+
+(Or remove the `--config=cuda` flag for running on CPU instead of GPU).
+
+On October 31, 2017, the benchmarks demonstrated comparable performance
+for eager and graph execution of this particular model when using
+a single NVIDIA Titan X (Pascal) GPU on a host with an
+Intel Xeon E5-1650 CPU @ 3.50GHz and a batch size of 32.
+
+| Benchmark name                           | batch size    | images/second |
+| ---------------------------------------  | ------------- | ------------- |
+| eager_train_gpu_batch_32_channels_first  |            32 |           171 |
+| graph_train_gpu_batch_32_channels_first  |            32 |           172 |