# Path: tensorflow_transform/nodes.py
# class ValueNode(
#     tfx_namedtuple.namedtuple('ValueNode',
#                               ['parent_operation', 'value_index'])):
# class OperationDef(metaclass=abc.ABCMeta):
# class OperationNode:
# class Visitor(metaclass=abc.ABCMeta):
# class Traverser:
# class _PrintGraphVisitor(Visitor):
#   def __init__(self, parent_operation, value_index: int):
#   def __iter__(self):
#   def num_outputs(self) -> int:
#   def label(self) -> str:
#   def get_field_str(self, field_name: str) -> str:
#   def is_partitionable(self) -> bool:
#   def cache_coder(self) -> Optional[object]:
#   def __init__(self, operation_def, inputs):
#   def __repr__(self):
#   def operation_def(self):
#   def inputs(self):
#   def outputs(self):
# def apply_operation(operation_def_cls, *args, **kwargs):
# def apply_multi_output_operation(operation_def_cls, *args, **kwargs):
#   def validate_value(self, value):
#   def visit(self, operation_def, input_values):
#   def __init__(self, visitor: Visitor):
#   def visit_value_node(self, value_node: ValueNode):
#   def _maybe_visit_value_node(self, value_node: ValueNode):
#   def _visit_operation(self, operation: OperationNode):
# def _escape(line: str) -> str:
#   def __init__(self):
#   def get_dot_graph(self) -> pydot.Dot:
#   def visit(self, operation_def, input_nodes) -> Tuple[pydot.Node, ...]:
#   def validate_value(self, value: pydot.Node):
# def get_dot_graph(leaf_nodes: Collection[ValueNode]) -> pydot.Dot:


import collections
import enum
import os
import uuid
import apache_beam as beam
from apache_beam.typehints import Union
from tensorflow_transform import nodes
from tfx_bsl.telemetry import util
from tfx_bsl.types import tfx_namedtuple
  value being registered may also be a function.  The registered function will
  be invoked as follows:

  outputs = my_function(inputs, operation, extra_args)

  where inputs, operation, extra_args and outputs are the same as for the
  PTransform case.

  Args:
    operation_def_subclass: The class of attributes that is being registered.
        Should be a subclass of `tensorflow_transform.nodes.OperationDef`.
    tags: A set of string tags belonging to `EnvironmentTags`. If
        provided, the PTransform will be registered against all of them.

  Returns:
    A class decorator that registers a PTransform or function as an
        implementation of the OperationDef subclass.
  """

  def register(ptransform_class):
    assert isinstance(ptransform_class, type)
    assert issubclass(ptransform_class, beam.PTransform)
    assert tags is None or (tag in _ALLOWED_PTRANSFORM_TAGS for tag in tags)
    _PTRANSFORM_BY_OPERATION_DEF_SUBCLASS[
        operation_def_subclass].add_ptransform(ptransform_class, tags)
    return ptransform_class

  return register



# Path: tensorflow_transform/nodes.py
# class ValueNode(
#     tfx_namedtuple.namedtuple('ValueNode',
#                               ['parent_operation', 'value_index'])):
# class OperationDef(metaclass=abc.ABCMeta):
# class OperationNode:
# class Visitor(metaclass=abc.ABCMeta):
# class Traverser:
# class _PrintGraphVisitor(Visitor):
#   def __init__(self, parent_operation, value_index: int):
#   def __iter__(self):
#   def num_outputs(self) -> int:
#   def label(self) -> str:
#   def get_field_str(self, field_name: str) -> str:
#   def is_partitionable(self) -> bool:
#   def cache_coder(self) -> Optional[object]:
#   def __init__(self, operation_def, inputs):
#   def __repr__(self):
#   def operation_def(self):
#   def inputs(self):
#   def outputs(self):
# def apply_operation(operation_def_cls, *args, **kwargs):
# def apply_multi_output_operation(operation_def_cls, *args, **kwargs):
#   def validate_value(self, value):
#   def visit(self, operation_def, input_values):
#   def __init__(self, visitor: Visitor):
#   def visit_value_node(self, value_node: ValueNode):
#   def _maybe_visit_value_node(self, value_node: ValueNode):
#   def _visit_operation(self, operation: OperationNode):
# def _escape(line: str) -> str:
#   def __init__(self):
#   def get_dot_graph(self) -> pydot.Dot:
#   def visit(self, operation_def, input_nodes) -> Tuple[pydot.Node, ...]:
#   def validate_value(self, value: pydot.Node):
# def get_dot_graph(leaf_nodes: Collection[ValueNode]) -> pydot.Dot:


# Path: tensorflow_transform/beam/common.py
import collections
import enum
import os
import uuid
import apache_beam as beam
from apache_beam.typehints import Union
from tensorflow_transform import nodes
from tfx_bsl.telemetry import util
from tfx_bsl.types import tfx_namedtuple

  value being registered may also be a function.  The registered function will
  be invoked as follows:

  outputs = my_function(inputs, operation, extra_args)

  where inputs, operation, extra_args and outputs are the same as for the
  PTransform case.

  Args:
    operation_def_subclass: The class of attributes that is being registered.
        Should be a subclass of `tensorflow_transform.nodes.OperationDef`.
    tags: A set of string tags belonging to `EnvironmentTags`. If
        provided, the PTransform will be registered against all of them.

  Returns:
    A class decorator that registers a PTransform or function as an
        implementation of the OperationDef subclass.
  """

  def register(ptransform_class):
    assert isinstance(ptransform_class, type)
    assert issubclass(ptransform_class, beam.PTransform)
    assert tags is None or (tag in _ALLOWED_PTRANSFORM_TAGS for tag in tags)
    _PTRANSFORM_BY_OPERATION_DEF_SUBCLASS[
        operation_def_subclass].add_ptransform(ptransform_class, tags)
    return ptransform_class

  return register



class ConstructBeamPipelineVisitor(nodes.Visitor):