BFalse bool    `xml:",attr"`
	BTrue  bool    `xml:",attr"`
	I      int     `xml:",attr"`
	INeg   int     `xml:",attr"`
	I8     int8    `xml:",attr"`
	I8Neg  int8    `xml:",attr"`
	I16    int16   `xml:",attr"`
	I16Neg int16   `xml:",attr"`
	I32    int32   `xml:",attr"`
	I32Neg int32   `xml:",attr"`
	I64    int64   `xml:",attr"`
	I64Neg int64   `xml:",attr"`
	UI     uint    `xml:",attr"`

}

Status ConvertAttribute(const mlir::BoolAttr& attr, AttrValue* value) {
  value->set_b(attr.getValue());
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::IntegerAttr& attr, AttrValue* value) {
  value->set_i(attr.getInt());
  return absl::OkStatus();
}

Status ConvertAttribute(const mlir::FloatAttr& attr, AttrValue* value) {
  value->set_f(attr.getValueAsDouble());
  return absl::OkStatus();
}

    op_state.addOperands(op.getOperands());
    op_state.addTypes(op.getResultTypes());

    SmallVector<NamedAttribute, 2> attrs;
    std::string parsed_op_name;
    tensorflow::NodeDef node_def;
    if (failed(ParseCustomOption(op.getCustomOption().getValue(), op.getLoc(),
                                 parsed_op_name, attrs, node_def))) {
      return failure();
    }
    if (parsed_op_name != tf_op_name) {
      return op.emitOpError(

    std::vector<NamedAttribute> attrs = contraction->getAttrs();
    ArrayAttr fused_ops_attr = ArrayAttr::get(context, fused_ops);
    attrs.push_back(
        NamedAttribute(StringAttr::get(context, "fused_ops"), fused_ops_attr));
    // Epsilon is used only in fusions with the FusedBatchNorm op, so we zero it
    // here.
    Attribute epsilon = rewriter.getF32FloatAttr(0);
    attrs.push_back(

              value.add_int64_val(dim.size());
            }
          }
          AddNodeAttr("value", value, &const_def);
          for (auto const& attr : n->attrs()) {
            if (*attr.first.begin() == '_') {
              AddNodeAttr(attr.first, attr.second, &const_def);
            }
          }

          TF_ASSIGN_OR_RETURN(Node * const_node, graph->AddNode(const_def));

        // Same type repeated "repeats" times.
        Attribute attr = attrs_[output_arg.type_attr()];
        if (!attr)
          return InvalidArgument("Missing attribute '", output_arg.type_attr(),
                                 "' required for output '", output_arg.name(),
                                 "'");
        TypedAttr type_attr = mlir::dyn_cast<TypedAttr>(attr);
        if (!type_attr)

# Attrs and Values

An [Attr] is a key-value pair. The Logger output methods accept Attrs as well as
alternating keys and values. The statement

	slog.Info("hello", slog.Int("count", 3))

behaves the same as

	slog.Info("hello", "count", 3)

There are convenience constructors for [Attr] such as [Int], [String], and [Bool]

  let description = [{
    The `attr` operation stores TF op's attribute, which doesn't support
    arithmetic operations.

    Example:

    ```mlir
    %1 = "tfr.constant"() { value: i32 } : () -> !tfr.attr
    %2 = "tfr.constant"() { value: [i32, f32] } : () -> !tfr.attr
    %3 = tfr.constant [i32, f32] -> !tfr.attr
    %4 = tfr.constant f32 -> !tfr.attr
    ```
  }];

to create tf2xla kernel: INVALID_ARGUMENT: NodeDef missing attrs 'shrink_axis_mask', 'new_axis_mask', 'begin_mask', 'ellipsis_mask', 'end_mask' from Op<name=StridedSlice; signature=input:T, begin:Index, end:Index, strides:Index -> output:T; attr=T:type; attr=Index:type,allowed=[DT_INT16, DT_INT32, DT_INT64]; attr=begin_mask:int,default=0; attr=end_mask:int,default=0; attr=ellipsis_mask:int,default=0; attr=new_axis_mask:int,default=0; attr=shrink_axis_mask:int,default=0>; NodeDef: {{node tf.StridedSlice}}}}...

    attrs = {
        "model_name": attr.string(),
        "srcs": attr.label_list(mandatory = True, allow_files = True),
        "header_out": attr.output(),
        "cmd": attr.string(),
        "tfcompile_tool": attr.label(cfg = "exec", executable = True, allow_files = True),
        "tfcompile_graph": attr.label(allow_single_file = True),
        "tfcompile_config": attr.label(allow_single_file = True),