}
    }

    /**
     * Maps [typeArguments] to the type parameters of [partiallyAppliedSymbol].
     *
     * If too many type arguments are provided, a mapping is still created. Extra type arguments are simply ignored. If this wasn't the
     * case, the resulting [KaCall] would contain no type arguments at all, which can cause problems later. If too few type arguments are

	hasmain uint8 // 1 if module contains the main function, 0 otherwise
	bad     bool  // module failed to load and should be ignored

	gcdatamask, gcbssmask bitvector

	typemap map[typeOff]*_type // offset to *_rtype in previous module

	next *moduledata
}

// A modulehash is used to compare the ABI of a new module or a
// package in a new module with the loaded program.
//

shared_name: the name by which this variable is referred to.
dtype and shape: attributes representing the data type and shape held in the
  variable.

Example:
    resource_variable_ops.var_handle_op(
          dtype=dtypes.int32, shape=[8, 16], container="foo", shared_name="bar")
  returns a handle for a variable with name "bar" in container "foo", and the
  variable holds a tensor of shape [8, 16] and dtype int32.
  }];

  let arguments = (ins

func (p *Package) recordTypedefs(dtype dwarf.Type, pos token.Pos) {
	p.recordTypedefs1(dtype, pos, map[dwarf.Type]bool{})
}

func (p *Package) recordTypedefs1(dtype dwarf.Type, pos token.Pos, visited map[dwarf.Type]bool) {
	if dtype == nil {
		return
	}
	if visited[dtype] {
		return
	}
	visited[dtype] = true
	switch dt := dtype.(type) {
	case *dwarf.TypedefType:

		if a.Name == obj.NAME_GOTREF {
			r.Siz = 4
			r.Type = objabi.R_GOTPCREL
		} else if useAbs(ctxt, s) {
			r.Siz = 4
			r.Type = objabi.R_ADDR
		} else {
			r.Siz = 4
			r.Type = objabi.R_PCREL
		}

		r.Off = -1 // caller must fill in
		r.Sym = s
		r.Add = a.Offset

		return 0
	}

	if (a.Type == obj.TYPE_MEM || a.Type == obj.TYPE_ADDR) && a.Reg == REG_TLS {
		if r == nil {

                                   OpBuilder &builder) {
  auto x_type = mlir::cast<RankedTensorType>(x.getType());
  auto y_type = mlir::cast<RankedTensorType>(y.getType());
  auto result_type = GetStaticBroadcastType(x_type, y_type, broadcast_dims);
  if (!result_type) {
    emitError(loc) << "could not binary broadcast " << x_type << ", " << y_type
                   << " with broadcast_dims = " << broadcast_dims;