# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

#ifdef __cplusplus
extern "C" {
#endif

// Resets `op_to_reset` with `op_or_function_name` and `raw_device_name`. This
// is for performance optimization by reusing an exiting unused op rather than
// creating a new op every time. If `raw_device_name` is `NULL` or empty, it
// does not set the device name. If it's not `NULL`, then it attempts to parse
// and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster

      : status_(TF_NewStatus()),
        // If the context's default exector is set to async, re-using that in
        // each thread would cause collectives to deadlock. For consistency we
        // create a new sync executor for every thread.
        //
        // TODO(allenl): We should have an async API that works with the
        // parallel device.
        device_(device),
        executor_(

//   reference types inside function's body to support legacy code. Do not
//   use them in new code.
// - Every node in the function's body must have all of its inputs (including
//   control inputs). In other words, for every node in the body, each input
//   must be either listed in `inputs` or must come from another node in

  Status Shape(const std::vector<int64_t>** shape) const;
  TF_DataType dtype() const { return dtype_; }

  // Sets its output argument to a summary of the values of this tensor on every
  // component device.
  Status SummarizeValue(std::string& summary);

  std::vector<TensorHandlePtr> release_tensors() { return std::move(tensors_); }

  std::vector<TFE_TensorHandle*> tensors() const {

  } else {
    return ToOperation(iter->second);
  }
}

TF_Operation* TF_GraphNextOperation(TF_Graph* graph, size_t* pos) {
  if (*pos == 0) {
    // Advance past the first sentinel nodes in every graph (the source & sink).
    *pos += 2;
  } else {
    // Advance to the next node.
    *pos += 1;
  }

  mutex_lock l(graph->mu);

    *   Added a new `rerandomize_each_iteration` argument for the `tf.data.Dataset.sample_from_datasets()` operation, which controls whether the...

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Release jobs are very basic. They don't use any caching or RBE,
# but they do upload logs to resultstore.
# IMPORTANT: trailing slash is required on GCS URIs, as it tells gcloud to
# pretend the path is a directory.
TFCI_ARTIFACT_FINAL_GCS_ENABLE=1

# This bazelrc can build a CPU-supporting TF package.

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

  "$python" -c 'import tensorflow as tf; t1=tf.constant([1,2,3,4]); t2=tf.constant([5,6,7,8]); print(tf.add(t1,t2).shape)'
  "$python" -c 'import sys; import tensorflow as tf; sys.exit(0 if "keras" in tf.keras.__name__ else 1)'
fi
# VERY basic check to ensure the [and-cuda] package variant is installable.
# Checks TFCI_BAZEL_COMMON_ARGS for "gpu" or "cuda", implying that the test is
# relevant. All of the GPU test machines have CUDA installed via other means,