TF_Buffer* buf,
                                                  TF_Status* status);

// Set logical devices to the context's device manager.
// If logical devices are already configured at context initialization
// through TFE_ContextOptions, this method should not be called.
TF_CAPI_EXPORT extern void TFE_SetLogicalCpuDevices(TFE_Context* ctx,

// o Calls to non-const methods require exclusive access to the object.
// o Concurrent calls to const methods are OK, if those calls are made while it
//   is guaranteed that no thread may call a non-const method.
//
// The logical function signature is:
//   ((unknown): f32[1,2], (unknown): s64[3,4], (unknown): f32[1], (unknown): f32[1], (unknown): s32[5]) -> (u32[5,6], f32[1], s32[5])
//
// Memory stats:
//   arg bytes total:    392

        // have been resolved.
        if (request.isResolveRoot()) {
            // Add the root artifact (as the first artifact to retain logical order of class path!)
            Set<Artifact> allArtifacts = new LinkedHashSet<>();
            allArtifacts.add(rootArtifact);
            allArtifacts.addAll(result.getArtifacts());

  oneof file {
    // Represents representative dataset saved as a .tfrecord file format.
    TfRecordFile tf_record = 1;
  }

  // [TF SavedModel] Identifies a SignatureDef which represents a single
  // logical function in a graph.
  optional string signature_key = 2;
}

// Preset config for static-range post-training quantization (PTQ).
//
// Minimal user input about representative datasets is required. Representative

  public final V remove(@CheckForNull Object rowKey, @CheckForNull Object columnKey) {
    throw new UnsupportedOperationException();
  }

  /**
   * Serialized type for all ImmutableTable instances. It captures the logical contents and
   * preserves iteration order of all views.
   */
  static final class SerializedForm implements Serializable {
    private final Object[] rowKeys;
    private final Object[] columnKeys;

	}
	e.err = e.w.WriteByte(b)
}

func (e *encoder) writeHeader() {
	if e.err != nil {
		return
	}
	_, e.err = io.WriteString(e.w, "GIF89a")
	if e.err != nil {
		return
	}

	// Logical screen width and height.
	byteorder.LePutUint16(e.buf[0:2], uint16(e.g.Config.Width))
	byteorder.LePutUint16(e.buf[2:4], uint16(e.g.Config.Height))
	e.write(e.buf[:4])

	spineCap  uintptr                   // Spine array cap, accessed under spineLock

	// index is the head and tail of the spanSet in a single field.
	// The head and the tail both represent an index into the logical
	// concatenation of all blocks, with the head always behind or
	// equal to the tail (indicating an empty set). This field is
	// always accessed atomically.
	//

    Pure,
    SameOperandsAndResultShape]> {
  let summary = "Logical NOT operator";

  let description = [{
    Element-wise logical NOT operation.
  }];

  let arguments = (ins TFL_BoolTensor:$lhs);

  let results = (outs TFL_BoolTensor:$output);
}

def TFL_LogicalOrOp : TFL_Op<"logical_or", [Pure]> {
  let summary = "Logical OR operator";

  let description = [{

        devices) {
  int64_t num_replicas = replicate.getN();
  assert(new_inputs.size() == num_replicas);

  // As model parallelism is not yet supported, we assume that all ops are
  // placed in logical core 0.
  // TODO(b/148913020): Remove this constraint once model parallelism is
  // supported.
  assert(devices.find(tensorflow::GetDeviceAliasForLogicalCore(0))
             ->getSecond()

}

def TPUShardingIdentificationPass : Pass<"tf-tpu-sharding-identification", "ModuleOp"> {
  let summary = "Identifies and handles inputs/outputs of TPU computation that is "
           "sharded across logical cores.";
  let constructor = "tensorflow::tf2xla::internal::CreateTPUShardingIdentificationPass()";
  let description = [{
    Bubbles up sharding configuration from `cluster_func` regions into