class_docstr: Base class for polymorphic graph functions.
  
  Graph functions are Python callable objects that dispatch calls to a
  TensorFlow graph. Polymorphic graph functions can be backed by multiple TF
  graphs, and automatically select the appropriate specialization based on the
  type of input they were called with. They may also create specializations on
  the fly if necessary, for example by tracing.
  
  Also see `tf.function`.
docstr: Returns a `ConcreteFunction` specialized to input types.
  
  The arguments specified by `args` and `kwargs` follow normal function call
  rules. The returned `ConcreteFunction` has the same set of positional and
  keyword arguments as `self`, but their types are compatible to the types
  specified by `args` and `kwargs` (though not neccessarily equal).
  
  >>> @tf.function
  ... def f(x):
  ...   return x
  >>> f_concrete = f.get_concrete_function(tf.constant(1.0))
  >>> f_concrete = f.get_concrete_function(x=tf.constant(1.0))
  
  Unlike normal calls, `get_concrete_function` allow type specifiers instead
  of TensorFlow objects, so for example `tf.Tensor`s may be replaced with
  `tf.TensorSpec`s.
  
  >>> @tf.function
  ... def f(x):
  ...   return x
  >>> f_concrete = f.get_concrete_function(tf.TensorSpec([], tf.float64))
  
  If the function definition allows only one specialization, `args` and
  `kwargs` may be omitted altogether.
  
  >>> @tf.function(input_signature=[tf.TensorSpec(None, tf.float32)])
  ... def f(x):
  ...   return x
  >>> f_concrete = f.get_concrete_function()
  
  The returned `ConcreteFunction` can be called normally:
  
  >>> f_concrete(tf.constant(1.0))
  <tf.Tensor: shape=(), dtype=float32, numpy=1.0>
  >>> f_concrete(x=tf.constant(1.0))
  <tf.Tensor: shape=(), dtype=float32, numpy=1.0>
  
  Args:
    *args: inputs to specialize on.
    **kwargs: inputs to specialize on.
  
  Returns:
    A `ConcreteFunction`.
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code: def get_concrete_function(self, *args, **kwargs) -> ConcreteFunction:
      """Returns a `ConcreteFunction` specialized to input types.
  
      The arguments specified by `args` and `kwargs` follow normal function call
      rules. The returned `ConcreteFunction` has the same set of positional and
      keyword arguments as `self`, but their types are compatible to the types
      specified by `args` and `kwargs` (though not neccessarily equal).
  
      >>> @tf.function
      ... def f(x):
      ...   return x
      >>> f_concrete = f.get_concrete_function(tf.constant(1.0))
      >>> f_concrete = f.get_concrete_function(x=tf.constant(1.0))
  
      Unlike normal calls, `get_concrete_function` allow type specifiers instead
      of TensorFlow objects, so for example `tf.Tensor`s may be replaced with
      `tf.TensorSpec`s.
  
      >>> @tf.function
      ... def f(x):
      ...   return x
      >>> f_concrete = f.get_concrete_function(tf.TensorSpec([], tf.float64))
  
      If the function definition allows only one specialization, `args` and
      `kwargs` may be omitted altogether.
  
      >>> @tf.function(input_signature=[tf.TensorSpec(None, tf.float32)])
      ... def f(x):
      ...   return x
      >>> f_concrete = f.get_concrete_function()
  
      The returned `ConcreteFunction` can be called normally:
  
      >>> f_concrete(tf.constant(1.0))
      <tf.Tensor: shape=(), dtype=float32, numpy=1.0>
      >>> f_concrete(x=tf.constant(1.0))
      <tf.Tensor: shape=(), dtype=float32, numpy=1.0>
  
      Args:
        *args: inputs to specialize on.
        **kwargs: inputs to specialize on.
  
      Returns:
        A `ConcreteFunction`.
      """
      pass
  
imports: import abc
  import inspect
  import sys
  import textwrap
  from typing import Union
  import numpy
  from tensorflow.python.types import doc_typealias
  from tensorflow.python import pywrap_tensorflow
  from tensorflow.python.util.tf_export import tf_export
label_desc: Use image node_id 1 for calling the PolymorphicFunction obj's underlying member method code with example usage: obj.get_concrete_function() without return types