# TODO(mdan): Index more efficiently. Could do a name check instead.
    if any(v is value for v in AG_MODULE.__dict__.values()):
      return {TFRTypes.AG_BUILTIN_FUNC}
    if getattr(value, '__name__', None) == 'tensorflow.raw_ops':
      return {types.ModuleType}
    if hasattr(value, '__module__'):
      if isinstance(value, dtypes.DType):
        return {TFRTypes.ATTR}

		}

		loc := fmt.Sprintf("%s%s.rules:%d", arch.name, suff, ruleLineno)
		for _, rule2 := range expandOr(rule) {
			r := Rule{Rule: rule2, Loc: loc}
			if rawop := strings.Split(rule2, " ")[0][1:]; isBlock(rawop, arch) {
				blockrules[rawop] = append(blockrules[rawop], r)
				continue
			}
			// Do fancier value op matching.
			match, _, _ := r.parse()
			op, oparch, _, _, _, _ := parseValue(match, arch, loc)

  def model_fn(args):
    # do something with dynamic tensor

  @function.Defun(capture_resource_var_by_value=False)
  def tpu_subgraph():
      return tf.tpu.rewrite(model_fn, args)

  return tf.raw_ops.TPUPartitionedCall(
      args=tpu_subgraph.captured_inputs,
      Tout=[o.type for o in tpu_subgraph.definition.signature.output_arg],
      f=tpu_subgraph,
      device_ordinal=[0])

elements in `x` must be complex numbers of the form \\(a + bj\\). The absolute
value is computed as \\( \sqrt{a^2 + b^2}\\).

For example:

>>> x = tf.complex(3.0, 4.0)
>>> print((tf.raw_ops.ComplexAbs(x=x, Tout=tf.dtypes.float32, name=None)).numpy())
5.0
  }];

  let arguments = (ins
    TensorOf<[TF_Complex128, TF_Complex64]>:$x
  );

  let results = (outs

// AllToAll op legalizations.
//===----------------------------------------------------------------------===//

// -----

// CHECK-LABEL: func @alltoall_basic
// See https://www.tensorflow.org/api_docs/python/tf/raw_ops/AllToAll
func.func @alltoall_basic(%input: tensor<1x2xf32>) -> tensor<2x1xf32> {
  %group_assignment = "tf.Const" () {
    value = dense<[[0, 1]]> : tensor<1x2xi32>
  } : () -> tensor<1x2xi32>