// CHECK:     %17 = "tfl.gather"(%arg13, %14) <{axis = 0 : i32, batch_dims = 0 : i32}> {tac.device = "DARWINN", tac.inference_type = "FLOAT"} : (tensor<5xi32>, tensor<?xi32>) -> tensor<?xi32>
// CHECK:     %18 = tfl.add %arg14, %17 {fused_activation_function = "NONE", tac.device = "DARWINN", tac.inference_type = "FLOAT"} : tensor<?xi32>

    // CHECK: %[[GATHER:.*]] = "stablehlo.gather"(%[[CALL]], %[[ARG2]])
    // CHECK-SAME: (tensor<3x4x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>, tensor<2x3x2xi64>) -> tensor<2x3x2x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>
    // CHECK: %[[DQ:.*]] = "quantfork.dcast"(%[[GATHER]]) : (tensor<2x3x2x2x!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<2x3x2x2xf32>

// (only `type` and one other matching field should be set at once).
message MetricSpec {
  // type is the type of metric source.  It should be one of "ContainerResource",
  // "External", "Object", "Pods" or "Resource", each mapping to a matching field in the object.
  // Note: "ContainerResource" type is available on when the feature-gate
  // HPAContainerMetrics is enabled
  optional string type = 1;

// (only `type` and one other matching field should be set at once).
message MetricSpec {
  // type is the type of metric source.  It should be one of "ContainerResource",
  // "External", "Object", "Pods" or "Resource", each mapping to a matching field in the object.
  // Note: "ContainerResource" type is available on when the feature-gate
  // HPAContainerMetrics is enabled
  optional string type = 1;

  // CHECK: %[[GATHER:.*]] = "tf.GatherV2"(%[[BUFFER]], %[[ARG1]], %[[AXIS]]) : (tensor<10x8x9xf32>, tensor<3xi32>, tensor<i32>) -> tensor<3x8x9xf32>
  %gather = "tf.TensorListGather"(%tl, %arg1, %elem_shape) : (tensor<!tf_type.variant<tensor<8x9xf32>>>, tensor<3xi32>, tensor<2xi32>) -> tensor<3x8x9xf32>
  // CHECK-NEXT: return %[[GATHER]] : tensor<3x8x9xf32>
  func.return %gather: tensor<3x8x9xf32>
}

// -----

// mhlo.dot_general:
//   1. flattened_out_dim = UnsortedSegmentProdOp(operand_shape, out_axes)
//   2. flattened_contracting_dim = UnsortedSegmentProdOp(operand_shape,
//   contracting_axes)
//   3. batch_dimensions = Gather(operand_shape, batch_axes)
//   4. flattened_shape = Concat(batch_dimensions, flattened_out_dim,
//   flattened_contracting_dim)
// The flattened shape for LHS
// is like [batch_dimensions, flattened_out_dimension,

      preset.enable_full_int_quantization() ? ".*"
                                            : "^.*(dot_general|gather).*");
  spec.mutable_method()->mutable_static_range_ptq();

  return spec;
}

QuantizationSpec GetDefaultWeightOnlyPtqSpec() {
  QuantizationSpec spec{};
  spec.mutable_matcher()->mutable_function_name()->set_regex(
      "^.*(conv|dot_general).*");

  parameters[
    {"quantized_ops": ["Gather"]}
  ]
  func.func @GenerateQuantizedFunctionName(${quantized_ops})(
                         %weight : tensor<*xi8>, %input : tensor<*xi32>, %axis : tensor<i32>,
                         %weight_scale : tensor<*xf32>, %weight_zp : tensor<*xi32>) -> tensor<*xf32>
      attributes {tf_quant.quantized_ops = ${quantized_ops}}
  {
    %accum_out = "tf.GatherV2"(%weight, %input, %axis) {

		}
	}
	if w != nil {
		return w.Flush()
	}
	return nil
}

func (s *XdsStatusWriter) setupStatusPrint(drs map[string]*discovery.DiscoveryResponse) (*tabwriter.Writer, []*xdsWriterStatus, error) {
	// Gather the statuses before printing so they may be sorted
	var fullStatus []*xdsWriterStatus
	mappedResp := map[string]string{}
	w := new(tabwriter.Writer).Init(s.Writer, 0, 8, 5, ' ', 0)

        Configuration codeCoverageResultsConf = configurations.resolvable("aggregateCodeCoverageReportResults").get();
        codeCoverageResultsConf.setDescription("Resolvable configuration used to gather files for the JaCoCo coverage report aggregation via ArtifactViews, not intended to be used directly");
        codeCoverageResultsConf.extendsFrom(jacocoAggregation);
        codeCoverageResultsConf.setVisible(false);