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

    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 = [{

  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])

        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

          (TF_TransposeOp $b, (TF_ConstOp (Get2DTransposePerm $transpose_b))),
          /*precision_config=*/(NullArrayAttr))>;

//===----------------------------------------------------------------------===//
// Logical & bitwise binary op patterns.
//===----------------------------------------------------------------------===//

class DirectLogicalBinaryPat<Op FromOp, Op ToOp>
  : Pat<(FromOp AnyTensor:$l, AnyTensor:$r),

  }

  /*
   * This class is used to serialize all ImmutableSet instances, except for
   * ImmutableEnumSet/ImmutableSortedSet, regardless of implementation type. It
   * captures their "logical contents" and they are reconstructed using public
   * static factories. This is necessary to ensure that the existence of a
   * particular implementation type is an implementation detail.
   */

import okhttp3.internal.toHeaderList
import okio.AsyncTimeout
import okio.Buffer
import okio.BufferedSource
import okio.Sink
import okio.Source
import okio.Timeout

/** A logical bidirectional stream. */
@Suppress("NAME_SHADOWING")
class Http2Stream internal constructor(
  val id: Int,
  val connection: Http2Connection,
  outFinished: Boolean,
  inFinished: Boolean,
  headers: Headers?,

   *       more reliable worst-case behavior.
   *   <li>null, if no entries have yet been added to the map
   * </ul>
   */
  @CheckForNull private transient Object table;

  /**
   * Contains the logical entries, in the range of [0, size()). The high bits of each int are the
   * part of the smeared hash of the element not covered by the hashtable mask, whereas the low bits