*       to check these for the most central aspects of functionality.) So, most tests use the
 *       smallest sensible numbers of threads, collection sizes, etc needed to check basic
 *       conformance.
 *   <li>The test classes currently do not declare inclusion in any particular package to simplify
 *       things for people integrating them in TCK test suites.

		Help:      "Distribution of object sizes in the bucket, includes label for the bucket name",
		Type:      histogramMetric,
	}
}

func getBucketObjectVersionsMD() MetricDescription {
	return MetricDescription{
		Namespace: bucketMetricNamespace,
		Subsystem: objectsSubsystem,
		Name:      versionDistribution,
		Help:      "Distribution of object sizes in the bucket, includes label for the bucket name",

	}
}

// Uploads an object using DummyDataGen directly via the http
// handler. Each part in a multipart object is a new DummyDataGen
// instance (so the part sizes are needed to reconstruct the whole
// object). When `len(partSizes) == 1`, asMultipart is used to upload
// the object as multipart with 1 part or as a regular single object.
//

and their sizes must match in all dimensions except `concat_dim`.}]>:$values
  );

  let results = (outs
    Res<TF_Tensor, [{A `Tensor` with the concatenation of values stacked along the
`concat_dim` dimension.  This tensor's shape matches that of `values` except
in `concat_dim` where it has the sum of the sizes.}]>:$output
  );

		// Counters for pod creation status (used by the job_pods_creation_total metric)
		var creationsSucceeded, creationsFailed int32 = 0, 0

		// Batch the pod creates. Batch sizes start at SlowStartInitialBatchSize
		// and double with each successful iteration in a kind of "slow start".
		// This handles attempts to start large numbers of pods that would

           "Maximum shape inference iterations">,
    Option<"input_arg_shapes_", "input-arg-shapes", "std::string", /*default=*/"",
           "Input tensor shapes. Shapes for different tensors are separated by ':', and dimension sizes for the same tensor are separated by ','">,
  ];
}

def StripNoinlineAttributePass : Pass<"tf-strip-noinline-attribute", "ModuleOp"> {
  let summary = "Strip the tf._noinline attribute from top-level functions.";

  return lhs.hasRank() && rhs.hasRank() && lhs.getRank() == rhs.getRank();
}

// Creates a compatible RankedTensorType where mismatched dimensions are
// replaced with dynamic sizes.
RankedTensorType GetCompatibleRankedTensorType(RankedTensorType lhs,
                                               RankedTensorType rhs) {
  assert(lhs.getRank() == rhs.getRank());
  llvm::SmallVector<int64_t, 4> dims;

	st := ctxt.makeRelocSymState()

	// This doesn't distinguish the memory size from the file
	// size, and it lays out the file based on Symbol.Value, which
	// is the virtual address. DWARF compression changes file sizes,
	// so dwarfcompress will fix this up later if necessary.
	eaddr := addr + size
	for _, s := range syms {
		if ldr.AttrSubSymbol(s) {
			continue
		}
		val := ldr.SymValue(s)
		if val >= eaddr {

*   Support for Python3.9 has been added.
*   `tf.data`:
    *   `tf.data` service now supports strict round-robin reads, which is useful
        for synchronous training workloads where example sizes vary. With strict
        round robin reads, users can guarantee that consumers get similar-sized
        examples in the same step.
    *   tf.data service now supports optional compression. Previously data would