from = partitionPoint + 1;
      }
    }
  }

  /**
   * Performs a partition operation on the slice of {@code array} with elements in the range [{@code
   * from}, {@code to}]. Uses the median of {@code from}, {@code to}, and the midpoint between them
   * as a pivot. Returns the index which the slice is partitioned around, i.e. if it returns {@code

        from = partitionPoint + 1;
      }
    }
  }

  /**
   * Performs a partition operation on the slice of {@code array} with elements in the range [{@code
   * from}, {@code to}]. Uses the median of {@code from}, {@code to}, and the midpoint between them
   * as a pivot. Returns the index which the slice is partitioned around, i.e. if it returns {@code

emulates renames through copy and delete APIs. This interaction pattern causes significant loss of performance because of the write amplification. _Netflix_, for example, developed two new staging committers - the Directory staging committer and the Partitioned staging committer - to take full advantage of native object storage operations. These committers do not require rename operation. The two staging committers were evaluated, along with another new addition called the Magic committer for benchmarking....

   * <p><b>Note:</b> if {@code iterable} is a {@link List}, use {@link Lists#partition(List, int)}
   * instead.
   *
   * @param iterable the iterable to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterable of unmodifiable lists containing the elements of {@code iterable} divided
   *     into partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive

  return %pi : !rtype
}
```
### `-tf-tpu-resource-partition`

_Partitions unpartitioned resource read/write to partitioned resource variables._

This pass creates individual resource reads/writes from the unpartitioned
resource variable (from `tf.TPUPartitionedInput`) to individual partitioned
resource variables (`tf.TPUPartitionedInput` operands). As resource op

   * <p><b>Note:</b> if {@code iterable} is a {@link List}, use {@link Lists#partition(List, int)}
   * instead.
   *
   * @param iterable the iterable to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterable of unmodifiable lists containing the elements of {@code iterable} divided
   *     into partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive

  }];

  let constructor = "tensorflow::tf2xla::internal::CreateExtractOutsideCompilationPass()";
}

def XlaClusterFormationPass : Pass<"tf-xla-cluster-formation", "ModuleOp"> {
  let summary = "Encapsulate partitioned calls within a Cluster op";
  let description = [{
    This pass clusters `tf.PartitionedCall` and `tf.StatefulPartitionedCall`
    with `_xla_compile_device_type` attribute into a `tf_device.cluster`.

//
// Each call to Session::Run() that identifies a new subgraph (based on feeds
// and fetches) creates some datastructures that live as long as the session
// (the partitioned graph, associated executors etc.).
//
// A pathological case of this would be if say the initialization op
// (main_op/legacy_init_op) involves the use of a large constant. Then we

  return llvm::formatv("requires attribute '{0}'", attribute).str();
}

// Returns nullptr if the op does not have a sharding attribute.
template <typename PartitionedOp>
mlir::Operation* NullUnlessSharded(PartitionedOp op) {
  return op.get_XlaSharding() ? op : nullptr;
}

// Returns a TPUPartitionedInput op connected to a `tf_device.cluster_func`

   * OutOfMemoryError}.
   *
   * @param iterator the iterator to return a partitioned view of
   * @param size the desired size of each partition (the last may be smaller)
   * @return an iterator of immutable lists containing the elements of {@code iterator} divided into
   *     partitions
   * @throws IllegalArgumentException if {@code size} is nonpositive
   */