// fetcher calls that the cache makes.
  calls.push_back(0);
  // Cache miss - drains an element from `calls`.
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  // Cache hit - does not drain an element from `calls`.
  TF_EXPECT_OK(ReadCache(&cache, "", 0, 1, &out));
  calls.push_back(block_size);
  // Cache miss followed by cache hit.
  TF_EXPECT_OK(ReadCache(&cache, "", block_size, 1, &out));

             'compilation to reduce the compilation time?'),
      'Eigen strong inline overridden.', 'Not overriding eigen strong inline, '
      'some compilations could take more than 20 mins.'):
    # Due to a known MSVC compiler issue
    # https://github.com/tensorflow/tensorflow/issues/10521
    # Overriding eigen strong inline speeds up the compiling of

`ModelServer` collates all computation graphs exposed to it (from multiple
`SavedModel`) and executes them in parallel on available executors. Running
TensorFlow in a multitenant design mixes the risks described above with the
inherent ones from multitenant configurations. The primary areas of concern are
tenant isolation, resource allocation, model sharing and hardware attacks.

### Tenant isolation

        ),
        cpu = "arm64-v8a",
    ),
    visibility = ["//visibility:public"],
)

config_setting(
    name = "android_mips",
    values = {
        "crosstool_top": "//external:android/crosstool",
        "cpu": "mips",
    },
    visibility = ["//visibility:public"],
)

config_setting(
    name = "android_mips64",
    values = {

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