`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

Since any tenants or users providing models, graphs or checkpoints can execute

  *object = fname.substr(bucket_end + 1);

  if (object->empty() && !object_empty_ok) {
    TF_SetStatus(status, TF_INVALID_ARGUMENT,
                 "GCS path doesn't contain an object name.");
  }
}

/// Appends a trailing slash if the name doesn't already have one.
static void MaybeAppendSlash(std::string* name) {
  if (name->empty())
    *name = "/";
  else if (name->back() != '/')
    name->push_back('/');
}

  /// operations.
  ///
  /// This operation must be provided. See "REQUIRED OPERATIONS" above.
  void (*cleanup)(TF_WritableFile* file);

  /// Appends `buffer` of size `n` to `*file`.
  ///
  /// Core TensorFlow owns `buffer` and guarantees at least `n` bytes of storage
  /// that can be used to write data.
  ///

*   `tf.random`:
    *   Various random number generation improvements:
    *   Add a fast path for default `random_uniform`
    *   `random_seed` documentation improvement.
    *   `RandomBinomial` broadcasts and appends the sample shape to the left
        rather than the right.
    *   Added `tf.random.stateless_binomial`, `tf.random.stateless_gamma`,
        `tf.random.stateless_poisson`