# LINT.IfChange
package_group(name = "structured_tensor_allowlist")
# LINT.ThenChange(copy.bara.sky)

filegroup(
    name = "intel_binary_blob",
    data = if_mkl_ml(
        [
            "@local_xla//xla/tsl/mkl:intel_binary_blob",
        ],
    ),
)

bzl_library(
    name = "tensorflow_bzl",
    srcs = ["tensorflow.bzl"],

            AVX512_BF16, AMX, etc.
            ([Intel Cascade Lake](https://www.intel.com/content/www/us/en/products/platforms/details/cascade-lake.html)
            and newer CPUs.)
            *   [Example performance speedups.](https://medium.com/intel-analytics-software/leverage-intel-deep-learning-optimizations-in-tensorflow-129faa80ee07)

  // Setting a larger thread pool does not help with the Swift caller, as we use
  // a different TFE context for each thread of execution (for running graph
  // functions, and their send/recvs corountines).
  config.set_inter_op_parallelism_threads(1);

  TF_Buffer* ret = TF_NewBuffer();
  TF_CHECK_OK(MessageToBuffer(config, ret));
  return ret;
}

TF_Buffer* TF_CreateRunOptions(unsigned char enable_full_trace) {