class_docstr: Represents the type of a TensorFlow function.
  
  FunctionType is the canonical way to represent the input/output contract of
  all kinds of functions within the tf.function domain, including:
    - Polymorphic Function
    - Concrete Function
    - Atomic Function
  
  It provides consistent, centralized and layered logic for:
    - Canonicalization of Python input arguments
    - Type-based dispatch to monomorphic functions
    - Packing/unpacking structured python values to Tensors
    - Generation of structured placeholder values for tracing
  
  Additionaly, it also provides:
    - Lossless serialization
    - Native integration with Python function signature representation
    - Seamless migration from older representation formats
docstr: Inspects and returns a dictionary of default values.
docstr_tok: ["Inspects","and","returns","a","dictionary","of","default","values","."]
code: def get_default_values(
      cls, obj: Callable[..., Any], *, follow_wrapped: bool = True
  ) -> Dict[str, Any]:
      """Inspects and returns a dictionary of default values."""
      signature = super().from_callable(
          obj, follow_wrapped=follow_wrapped
      )
      default_values = {}
      for p in signature.parameters.values():
          if p.default is not p.empty:
              default_values[p.name] = p.default
      return default_values
  
imports: import collections
  import inspect
  from typing import Any, Callable, Dict, List, Mapping, Optional, Sequence, Tuple
  from absl import logging
  from tensorflow.core.function import trace_type
  from tensorflow.core.function.polymorphism import function_type_pb2
  from tensorflow.core.function.trace_type import serialization
  from tensorflow.python.types import core
  from tensorflow.python.types import trace
label_desc: Use image node_id 6 for calling the FunctionType obj's underlying member method code with example usage: obj.get_default_values(cls, obj) and returns: default_values