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# tf.function

Compiles a function into a callable TensorFlow graph.

`tf.function` constructs a callable that executes a TensorFlow graph (`tf.Graph`) created by trace-compiling the TensorFlow operations in `func`, effectively executing `func` as a TensorFlow graph.

#### Example usage:

````@tf.function`
`def f(x, y):`
`  return x ** 2 + y`
`x = tf.constant([2, 3])`
`y = tf.constant([3, -2])`
`f(x, y)`
`<tf.Tensor: ... numpy=array([7, 7], ...)>`
```

Features

`func` may use data-dependent control flow, including `if`, `for`, `while` `break`, `continue` and `return` statements:

````@tf.function`
`def f(x):`
`  if tf.reduce_sum(x) > 0:`
`    return x * x`
`  else:`
`    return -x // 2`
`f(tf.constant(-2))`
`<tf.Tensor: ... numpy=1>`
```

`func`'s closure may include `tf.Tensor` and `tf.Variable` objects:

````@tf.function`
`def f():`
`  return x ** 2 + y`
`x = tf.constant([-2, -3])`
`y = tf.Variable([3, -2])`
`f()`
`<tf.Tensor: ... numpy=array([7, 7], ...)>`
```

`func` may also use ops with side effects, such as `tf.print`, `tf.Variable` and others:

````v = tf.Variable(1)`
`@tf.function`
`def f(x):`
`  for i in tf.range(x):`
`    v.assign_add(i)`
`f(3)`
`v`
`<tf.Variable ... numpy=4>`
```
````l = []`
`@tf.function`
`def f(x):`
`  for i in x:`
`    l.append(i + 1)    # Caution! Will only happen once when tracing`
`f(tf.constant([1, 2, 3]))`
`l`
`[<tf.Tensor ...>]`
```

Instead, use TensorFlow collections like `tf.TensorArray`:

````@tf.function`
`def f(x):`
`  ta = tf.TensorArray(dtype=tf.int32, size=0, dynamic_size=True)`
`  for i in range(len(x)):`
`    ta = ta.write(i, x[i] + 1)`
`  return ta.stack()`
`f(tf.constant([1, 2, 3]))`
`<tf.Tensor: ..., numpy=array([2, 3, 4], ...)>`
```

`tf.function` is polymorphic

Internally, `tf.function` can build more than one graph, to support arguments with different data types or shapes, since TensorFlow can build more efficient graphs that are specialized on shapes and dtypes. `tf.function` also treats any pure Python value as opaque objects, and builds a separate graph for each set of Python arguments that it encounters.

To obtain an individual graph, use the `get_concrete_function` method of the callable created by `tf.function`. It can be called with the same arguments as `func` and returns a special `tf.Graph` object:

````@tf.function`
`def f(x):`
`  return x + 1`
`isinstance(f.get_concrete_function(1).graph, tf.Graph)`
`True`
```
````@tf.function`
`def f(x):`
`  return tf.abs(x)`
`f1 = f.get_concrete_function(1)`
`f2 = f.get_concrete_function(2)  # Slow - builds new graph`
`f1 is f2`
`False`
`f1 = f.get_concrete_function(tf.constant(1))`
`f2 = f.get_concrete_function(tf.constant(2))  # Fast - reuses f1`
`f1 is f2`
`True`
```

Python numerical arguments should only be used when they take few distinct values, such as hyperparameters like the number of layers in a neural network.

Input signatures

For Tensor arguments, `tf.function` instantiates a separate graph for every unique set of input shapes and datatypes. The example below creates two separate graphs, each specialized to a different shape:

````@tf.function`
`def f(x):`
`  return x + 1`
`vector = tf.constant([1.0, 1.0])`
`matrix = tf.constant([[3.0]])`
`f.get_concrete_function(vector) is f.get_concrete_function(matrix)`
`False`
```

An "input signature" can be optionally provided to `tf.function` to control the graphs traced. The input signature specifies the shape and type of each Tensor argument to the function using a `tf.TensorSpec` object. More general shapes can be used. This is useful to avoid creating multiple graphs when Tensors have dynamic shapes. It also restricts the dhape and datatype of Tensors that can be used:

````@tf.function(`
`    input_signature=[tf.TensorSpec(shape=None, dtype=tf.float32)])`
`def f(x):`
`  return x + 1`
`vector = tf.constant([1.0, 1.0])`
`matrix = tf.constant([[3.0]])`
`f.get_concrete_function(vector) is f.get_concrete_function(matrix)`
`True`
```

Variables may only be created once

`tf.function` only allows creating new `tf.Variable` objects when it is called for the first time:

````class MyModule(tf.Module):`
`  def __init__(self):`
`    self.v = None`

`  @tf.function`
`  def call(self, x):`
`    if self.v is None:`
`      self.v = tf.Variable(tf.ones_like(x))`
`    return self.v * x`
```

In general, it is recommended to create stateful objects like `tf.Variable` outside of `tf.function` and passing them as arguments.

`func` the function to be compiled. If `func` is None, `tf.function` returns a decorator that can be invoked with a single argument - `func`. In other words, `tf.function(input_signature=...)(func)` is equivalent to `tf.function(func, input_signature=...)`. The former can be used as decorator.
`input_signature` A possibly nested sequence of `tf.TensorSpec` objects specifying the shapes and dtypes of the Tensors that will be supplied to this function. If `None`, a separate function is instantiated for each inferred input signature. If input_signature is specified, every input to `func` must be a `Tensor`, and `func` cannot accept `**kwargs`.
`autograph` Whether autograph should be applied on `func` before tracing a graph. Data-dependent control flow requires `autograph=True`. For more information, see the tf.function and AutoGraph guide.
`experimental_implements` If provided, contains a name of a "known" function this implements. For example "mycompany.my_recurrent_cell". This is stored as an attribute in inference function, which can then be detected when processing serialized function. See https://github.com/tensorflow/community/blob/master/rfcs/20190610-standardizing-composite_ops.md for details. For an example of utilizing this attribute see: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/compiler/mlir/lite/transforms/prepare_composite_functions_tf.cc The code above automatically detects and substitutes function that implements "embedded_matmul" and allows TFLite to substitute its own implementations. For instance, a tensorflow user can use this attribute to mark that their function also implements `embedded_matmul``` (perhaps more efficiently!) by specifying it using this flag.

``````@tf.function(experimental_implements="embedded_matmul"):
def embedding_matmul(a, b):
# custom implementation here
``````

`experimental_autograph_options` Optional tuple of `tf.autograph.experimental.Feature` values.
`experimental_relax_shapes` When True, `tf.function` may generate fewer, graphs that are less specialized on input shapes.
`experimental_compile` If True, the function is always compiled by XLA. XLA may be more efficient in some cases (e.g. TPU, XLA_GPU, dense tensor computations).

If `func` is not None, returns a callable that will execute the compiled function (and return zero or more `tf.Tensor` objects). If `func` is None, returns a decorator that, when invoked with a single `func` argument, returns a callable equivalent to the case above.

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