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:

    define_values = {"__TENSORFLOW_NO_LGPL_DEPS__": "1"},
    visibility = ["//visibility:public"],
)

# Config setting that disables the default logger, only logging
# to registered TFLogSinks
config_setting(
    name = "no_default_logger",
    define_values = {"no_default_logger": "true"},
    visibility = ["//visibility:public"],
)

config_setting(
    name = "force_rtti",
    define_values = {

      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`