: test_mode_(false), calib_opts_(calib_opts) {}

  InsertCustomAggregationOpsPass(const InsertCustomAggregationOpsPass &other) {
    test_mode_ = other.test_mode_;
    test_case_ = other.test_case_;
    calib_opts_ = other.calib_opts_;
    initializeForTest();
  }

  MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(InsertCustomAggregationOpsPass)

If you suspect issues with your plugin code, try creating a link:https://scans.gradle.com/[Build Scan] to identify bottlenecks.

source "${KUBE_ROOT}/hack/lib/init.sh"

register_files=()
while read -r file ; do
	register_files+=("${file}")
done < <(find pkg/apis -name register.go | sort)

# every register file should contain a GroupName.  Gather the different representations.
# 1. group directory name for client gen
# 2. external group versions for init.sh all APIs list
# 3. install packages for inclusion in import_known_versions files
group_dirnames=()

// designate the dimension indices that were collapsed to produce `tensor_type`.
// The restored dimensions' sizes are 1, according to the semantics of
// `XlaGatherOp (https://www.tensorflow.org/xla/operation_semantics#gather). The
// resulting type's shape has `tensor_type.size() + collapsed_dims.size()`
// dimensions.
RankedTensorType RestoreCollapsedDimensions(
    const RankedTensorType tensor_type,

// 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>

// 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).*");

@app.route('/logout', methods=['GET'])
def logout():
    response = app.make_response(redirect(request.referrer))
    session.pop('user', None)
    return response

# a helper function for asyncio.gather, does not return a value


async def getProductReviewsIgnoreResponse(product_id, headers):
    getProductReviews(product_id, headers)

            if (lockingState.mustValidateLockState() && !lockingState.getLockedDependencies().isEmpty()) {
                // Invalid lock state, need to do a real resolution to gather locking failures
                return delegate.resolveGraph(resolveContext);
            }