* index.
   *
   * <p>Its size must be a power of two.
   */
  private transient int[] table;

  /**
   * Contains the logical entries, in the range of [0, size()). The high 32 bits of each long is the
   * smeared hash of the element, whereas the low 32 bits is the "next" pointer (pointing to the

  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

                   TF::EnqueueTPUEmbeddingRaggedTensorBatchOp,
                   TF::EnqueueTPUEmbeddingArbitraryTensorBatchOp>(op);
}

// Returns the device ordinal (`device_ordinal`) for a replica (`replica_id`)
// and logical core (`logical_core`).
// `replica_id` is the index of the ancestor ReplicateOp in [0, num_replicas).
// `logical_core` is the index of the TPU core in [0, num_cores_per_replica).

  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;

				result[j] = p.embeddingLevel
			} else {
				break
			}
		}
		start = limit
	}

	return result
}

// getReordering returns the reordering of lines from a visual index to a
// logical index for line breaks at the given offsets.
//
// Lines are concatenated from left to right. So for example, the fifth
// character from the left on the third line is
//
//	getReordering(linebreaks)[linebreaks[1] + 4]

    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

type NetworkPolicyIngressRule struct {
	// List of ports which should be made accessible on the pods selected for this rule.
	// Each item in this list is combined using a logical OR.
	// If this field is empty or missing, this rule matches all ports (traffic not restricted by port).
	// If this field is present and contains at least one item, then this rule allows traffic

In Go asm, the last operand (right) is the target operand, but with PPC64 asm,
the first operand (left) is the target. The order of the remaining operands is
not consistent: in general opcodes with 3 operands that perform math or logical
operations have their operands in reverse order. Opcodes for vector instructions
and those with more than 3 operands usually have operands in the same order except