class_docstr: Represents the type of object(s) for tf.function tracing purposes.
  
  `TraceType` is an abstract class that other classes might inherit from to
  provide information regarding associated class(es) for the purposes of
  tf.function tracing. The typing logic provided through this mechanism will be
  used to make decisions regarding usage of cached concrete functions and
  retracing.
  
  For example, if we have the following tf.function and classes:
  ```python
  @tf.function
  def get_mixed_flavor(fruit_a, fruit_b):
    return fruit_a.flavor + fruit_b.flavor
  
  class Fruit:
    flavor = tf.constant([0, 0])
  
  class Apple(Fruit):
    flavor = tf.constant([1, 2])
  
  class Mango(Fruit):
    flavor = tf.constant([3, 4])
  ```
  
  tf.function does not know when to re-use an existing concrete function in
  regards to the `Fruit` class so naively it retraces for every new instance.
  ```python
  get_mixed_flavor(Apple(), Mango()) # Traces a new concrete function
  get_mixed_flavor(Apple(), Mango()) # Traces a new concrete function again
  ```
  
  However, we, as the designers of the `Fruit` class, know that each subclass
  has a fixed flavor and we can reuse an existing traced concrete function if
  it was the same subclass. Avoiding such unnecessary tracing of concrete
  functions can have significant performance benefits.
  
  ```python
  class FruitTraceType(tf.types.experimental.TraceType):
    def __init__(self, fruit):
      self.fruit_type = type(fruit)
      self.fruit_value = fruit
  
    def is_subtype_of(self, other):
       return (type(other) is FruitTraceType and
               self.fruit_type is other.fruit_type)
  
    def most_specific_common_supertype(self, others):
       return self if all(self == other for other in others) else None
  
    def placeholder_value(self, placeholder_context=None):
      return self.fruit_value
  
  class Fruit:
  
   def __tf_tracing_type__(self, context):
     return FruitTraceType(self)
  ```
  
  Now if we try calling it again:
  ```python
  get_mixed_flavor(Apple(), Mango()) # Traces a new concrete function
  get_mixed_flavor(Apple(), Mango()) # Re-uses the traced concrete function
  ```
docstr: Creates a placeholder for tracing.
  
  tf.funcion traces with the placeholder value rather than the actual value.
  For example, a placeholder value can represent multiple different
  actual values. This means that the trace generated with that placeholder
  value is more general and reusable which saves expensive retracing.
  
  Args:
    placeholder_context: A context reserved for internal/future usage.
  For the `Fruit` example shared above, implementing:
  
  ```python
  class FruitTraceType:
    def placeholder_value(self, placeholder_context):
      return Fruit()
  ```
  instructs tf.function to trace with the `Fruit()` objects
  instead of the actual `Apple()` and `Mango()` objects when it receives a
  call to `get_mixed_flavor(Apple(), Mango())`. For example, Tensor arguments
  are replaced with Tensors of similar shape and dtype, output from
  a tf.Placeholder op.
  
  More generally, placeholder values are the arguments of a tf.function,
  as seen from the function's body:
  ```python
  @tf.function
  def foo(x):
    # Here `x` is be the placeholder value
    ...
  
  foo(x) # Here `x` is the actual value
  ```
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code: def placeholder_value(self, placeholder_context) -> Any:
      """Creates a placeholder for tracing.
  
      tf.funcion traces with the placeholder value rather than the actual value.
      For example, a placeholder value can represent multiple different
      actual values. This means that the trace generated with that placeholder
      value is more general and reusable which saves expensive retracing.
  
      Args:
        placeholder_context: A context reserved for internal/future usage.
      For the `Fruit` example shared above, implementing:
  
      ```python
      class FruitTraceType:
        def placeholder_value(self, placeholder_context):
          return Fruit()
      ```
      instructs tf.function to trace with the `Fruit()` objects
      instead of the actual `Apple()` and `Mango()` objects when it receives a
      call to `get_mixed_flavor(Apple(), Mango())`. For example, Tensor arguments
      are replaced with Tensors of similar shape and dtype, output from
      a tf.Placeholder op.
  
      More generally, placeholder values are the arguments of a tf.function,
      as seen from the function's body:
      ```python
      @tf.function
      def foo(x):
        # Here `x` is be the placeholder value
        ...
  
      foo(x) # Here `x` is the actual value
      ```
      """
  
imports: import abc
  from typing import Any, Iterator, List, Optional, Sequence
  from typing_extensions import Protocol
  from typing_extensions import runtime_checkable
  from tensorflow.python.types import core
  from tensorflow.python.util.tf_export import tf_export
  from tensorflow.tools.docs import doc_controls
label_desc: Use image node_id 3 for calling the TraceType obj's underlying member method code with example usage: obj.placeholder_value(placeholder_context) without return types