ModuleOp module_op = op->getParentOfType<ModuleOp>();

    // Ignore ops without quantization method.
    // Consider adding checks for individual methods.
    if (!op->getAttr(kQuantizationMethodAttr)) return failure();

    // Ignore unquantized ops.
    if (!IsQuantizedXlaCallModuleOp(op)) return failure();

    // For weight-only quantization, op should be hybrid quantized.

    }
    // Recreate the attributes dictionary to only contain "value".
    NamedAttrList attributes;
    attributes.append(NamedAttribute(value_str_attr, cst->getAttr("value")));
    cst->setAttrs(attributes.getDictionary(&getContext()));
  });

  auto& side_effect_analysis = getAnalysis<mlir::TF::SideEffectAnalysis>();
  for (auto func : getOperation().getOps<mlir::func::FuncOp>())

      for (const auto& attr_name : attr_names) {
        if (!global_iter_id_op->hasAttr(attr_name)) continue;
        offset->setAttr(attr_name, global_iter_id_op->getAttr(attr_name));
        new_global_iter_id->setAttr(attr_name,
                                    global_iter_id_op->getAttr(attr_name));
      }
      // Make the next function to get inlined use a different offset.
      ++offset_value;
    }

  if (padding == tensorflow::Padding::EXPLICIT) {
    ArrayRef<Attribute> explicit_padding;
    ArrayAttr explicit_pad = mlir::dyn_cast_or_null<::mlir::ArrayAttr>(
        op->getAttr("explicit_paddings"));
    if (!explicit_pad) {
      explicit_pad = ::mlir::Builder(op->getContext()).getI64ArrayAttr({});
    }
    explicit_padding = explicit_pad.getValue();

           llvm::all_of(op->getResultTypes(), is_not_variant);
  };

  auto is_set_item_legal = [](Operation *op) {
    return op->hasAttr("resize_if_index_out_of_bounds") &&
           op->getAttr("resize_if_index_out_of_bounds")
               .cast<mlir::BoolAttr>()
               .getValue();
  };

  ConversionTarget target(*context);

    // folding), but we can still derive the shape of a constant tensor for
    // its attribute type.
    auto tensor_attr = mlir::cast<mlir::TypedAttr>(inst->getAttr("value"));
    llvm::ArrayRef<int64_t> shape_ref =
        mlir::cast<TensorType>(tensor_attr.getType()).getShape();
    if (mlir::failed(check_shape(shape_ref))) return std::nullopt;

    // Calculate the scaling that needs to be applied to the iota.
    auto step_numerator = rewriter.create<chlo::BroadcastSubOp>(
        op.getLoc(), op.getStart().getType(), op.getStop(), op.getStart(),
        hlo::getBroadcastDimensionsAttr(&rewriter, op.getStop(),
                                        op.getStart()));
    Value step_denominator = rewriter.create<ConvertOp>(
        op.getLoc(), op.getNum(), result_type.getElementType());

Method <org.gradle.api.tasks.scala.ScalaDocOptions.getHeader()> does not have raw return type assignable to org.gradle.api.provider.Property in (ScalaDocOptions.java:0)
Method <org.gradle.api.tasks.scala.ScalaDocOptions.getTop()> does not have raw return type assignable to org.gradle.api.provider.Property in (ScalaDocOptions.java:0)

template <typename CallOpClass>
static LogicalResult VerifyPartitionedCall(CallOpClass op,
                                           SymbolTableCollection &symbolTable) {
  SymbolRefAttr func = op->getAttr("f").template cast<SymbolRefAttr>();
  auto function = symbolTable.lookupNearestSymbolFrom<func::FuncOp>(op, func);
  if (!function) {
    return op.emitError("'f' attribute refers to an undefined function: ")

	tests := []struct {
		name string
		req  *Request
		want bool
	}{
		{
			name: "GET",
			req:  &Request{Method: "GET"},
			want: true,
		},
		{
			name: "GET_http.NoBody",
			req:  &Request{Method: "GET", Body: NoBody},
			want: true,
		},
		{
			name: "GET_body",
			req:  &Request{Method: "GET", Body: someBody},
			want: false,
		},
		{