}

	// Correlate the symbols from the binary with the profile samples.
	for _, s := range syms {
		sns := symNodes[s]

		// Gather samples for this symbol.
		flatSum, cumSum := sns.Sum()

		// Get the function assembly.
		insts, err := obj.Disasm(s.sym.File, s.sym.Start, s.sym.End, o.IntelSyntax)
		if err != nil {
			return err
		}

func.func @testGather(%arg0 : tensor<?xf32>, %arg1 : tensor<?xi32>) -> tensor<?xf32> {
  %0 = "tfl.gather"(%arg0, %arg1) {axis = 1 : i32}: (tensor<?xf32>,tensor<?xi32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
}

// -----

// CHECK-LABEL: testGather
func.func @testGather(%arg0 : tensor<2xf32>, %arg1 : tensor<2xi32>) -> tensor<2xf32> {
  %0 = "tfl.gather"(%arg0, %arg1) {axis = 1 : i32}: (tensor<2xf32>,tensor<2xi32>) -> tensor<2xf32>

					}
					r.Shuffle(n, func(i, j int) { p[i], p[j] = p[j], p[i] })
					return encodePerm(p)
				}},
			}

			for _, test := range tests {
				t.Run(test.name, func(t *testing.T) {
					// Gather chi-squared values and check that they follow
					// the expected normal distribution given n!-1 degrees of freedom.
					// See https://en.wikipedia.org/wiki/Pearson%27s_chi-squared_test and

			return -1
		}
		return val.AuxInt
	}

	// Element size of the individual stores.
	size := root.Aux.(*types.Type).Size()
	if size*n > root.Block.Func.Config.RegSize {
		return false
	}

	// Gather n stores to look at. Check easy conditions we require.
	a := make([]StoreRecord, 0, 8)
	rbase, roff := splitPtr(root.Args[0])
	if root.Block.Func.Config.arch == "S390X" {

  // When unset, just the metricName will be used to gather metrics.
  // +optional
  optional k8s.io.apimachinery.pkg.apis.meta.v1.LabelSelector selector = 2;
}

// MetricSpec specifies how to scale based on a single metric
// (only `type` and one other matching field should be set at once).
message MetricSpec {

  // When unset, just the metricName will be used to gather metrics.
  // +optional
  optional .k8s.io.apimachinery.pkg.apis.meta.v1.LabelSelector selector = 2;
}

// MetricSpec specifies how to scale based on a single metric
// (only `type` and one other matching field should be set at once).
message MetricSpec {

}

func (MetricIdentifier) SwaggerDoc() map[string]string {
	return map_MetricIdentifier
}

var map_MetricSpec = map[string]string{
	"":                  "MetricSpec specifies how to scale based on a single metric (only `type` and one other matching field should be set at once).",

  // When unset, just the metricName will be used to gather metrics.
  // +optional
  optional .k8s.io.apimachinery.pkg.apis.meta.v1.LabelSelector selector = 2;
}

// MetricSpec specifies how to scale based on a single metric
// (only `type` and one other matching field should be set at once).
message MetricSpec {

  //   f = scale * (q - zero_point)
  // For other quantization types, the QuantizationDetails below is used.
  min:[float];  // For importing back into tensorflow.
  max:[float];  // For importing back into tensorflow.
  scale:[float];  // For dequantizing the tensor's values.
  zero_point:[long];

  // If this is not none, the other quantization parameters (i.e. min, max,

        during the python Dataset object creation instead doing it during
        Iterator creation time.
    *   Add `batch_dims` argument to `tf.gather`.
    *   The behavior of `tf.gather` is now correct when `axis=None` and
        `batch_dims<0`.
    *   Update docstring for gather to properly describe the non-empty
        `batch_dims` case.
    *   Removing of dtype in the constructor of initializers and partition_info