-----------------------------------------------------------------------------------------------
   
   **2. Installing **TensorFlow** (TF) CPU prebuilt binaries**
   
   
   *TensorFlow release binaries version 1.6 and higher are prebuilt with AVX instruction sets.*
   
   
   Therefore on any CPU that does not have these instruction sets, either CPU or GPU version of TF will fail to load.
   

  `comp:` etc.  At this stage we check if the PR is valid and meets certain
  quality requirements. For example, we check if the CLA is signed, PR has
  sufficient description, if applicable unit tests are added, if it is a
  reasonable contribution (meaning it is not a single liner cosmetic PR).

**2. Valid?**

-   If the PR passes all the quality checks then we go ahead and assign a
    reviewer.

  ///    filesystem failed.
  /// 2) `TF_FAILED_PRECONDITION` if the read from the remote filesystem
  /// succeeded,
  ///    but the read returned a partial block, and the LRU cache contained a
  ///    block at a higher offset (indicating that the partial block should have
  ///    been a full block).
  /// 3) `TF_OUT_OF_RANGE` if the read from the remote filesystem succeeded, but

    return -1;
  }
  int64_t read;
  auto content_length = stream.headers().find("content-length");
  if (content_length == stream.headers().end()) {
    // When we read a file with offset that is bigger than the actual file size.
    // GCS will return an empty header (e.g no `content-length` header). In this
    // case, we will set read to `0` and continue.
    read = 0;

      control the initial parallelism setting used by autotune before the data
      pipeline has started running. The default is 16. A lower value reduces
      initial memory usage, while a higher value improves startup time.

## Keras

*  `keras.layers.experimental.DynamicEmbedding`
    * Added `DynamicEmbedding` Keras layer
    * Added 'UpdateEmbeddingCallback`

  status = env_->NewRandomAccessFile(filepath, &read_file);
  if (!status.ok())
    GTEST_SKIP() << "NewRandomAccessFile() not supported: " << status;

  char scratch[64 /* must be bigger than test_data */] = {0};
  StringPiece result;
  // read at least 1 byte more than test_data
  status = read_file->Read(0, test_data.size() + 1, &result, scratch);

        else:
          ver = int(sm_compute_match.group(2))
          if ver < 30:
            print(
                'ERROR: TensorFlow only supports small CUDA compute'
                ' capabilities of sm_30 and higher. Please re-specify the list'
                ' of compute capabilities excluding version %s.' % ver)
            all_valid = False
          if ver < 35: