## lib/nx/defn.ex

``````defmodule Nx.Defn do
@moduledoc ~S"""
Numerical functions.

A numerical function is a subset of Elixir tailored for
numerical computations. For example, the following function:

defmodule MyModule do
import Nx.Defn

defn softmax(t) do
Nx.exp(t) / Nx.sum(Nx.exp(t))
end
end

will work with scalars, vector, matrices, and n-dimensional
tensors. Depending on your compiler of choice, the code can even
be JIT-compiled and run either on the CPU or GPU.

To support these features, `defn` is a subset of Elixir. It
replaces Elixir's `Kernel` by `Nx.Defn.Kernel`. `Nx.Defn.Kernel`
provides tensor-aware operators, such as `+`, `-`, etc, while
also preserving many high-level constructs known to Elixir
developers, such as pipe operator, aliases, conditionals,
pattern-matching, the access syntax, and more:

For example, the code above can also be written as:

defmodule MyModule do
import Nx.Defn

defn softmax(t) do
t
|> Nx.exp(t)
|> then(& &1 / Nx.sum(&1))
end
end

Please consult `Nx.Defn.Kernel` for a complete reference.

## Operators

`defn` attempts to keep as close to the Elixir semantics as
possible but that's not achievable. For example, mathematical
and bitwise operators (`+`, `-`, `&&&`, `<<<`, etc.) in Elixir
work on numbers, which means mapping them to tensors is
straight-forward and they largely preserve the same semantics,
except they are now multi-dimensional.

On the other hand, the logical operators `and`, `or`, and `not`
work with booleans in Elixir (`true` and `false`), which map
to `0` and `1` in `defn`.

Therefore, when working with logical operators inside `defn`,
`0` is considered `false` and all other numbers are considered
`true`, which is represented as the number `1`. For example, in
`defn`, `0 and 1` as well as `0 and 2` return `0`, while
`1 and 1` or `1 and -1` will return `1`.

The same semantics apply to conditional expressions inside `defn`,
such as `if`, `while`, etc.

## JIT compilers

The power of `Nx.Defn` is given by its compilers. The default
compiler is `Nx.Defn.Evaluator`, which evalutes the code.
You can use `jit/3` to compile a function on the fly using a
different compiler, such as `EXLA`:

fun = Nx.Defn.jit(&MyModule.softmax/1, compiler: EXLA)
fun.(my_tensor)

The above will return an anonymous function that optimizes,
compiles, and run `softmax` on the fly on the CPU (or the GPU)
if available.

You can also change the default compiler for all numerical
definitions (`defn`) by setting the default options. This can
be done in your `config/*.exs` files as follows:

config :nx, :default_defn_options, compiler: EXLA

Now calling `MyModule.softmax(my_tensor)` will use `EXLA` even
without wrapping it in `jit/2`.

However, note that compilation may be quite time consuming on
the first invocation, that's why it is often preferred to use
the `compiler: EXLA` option when calling the functions in this
module instead. EXLA, in particular, also exports a `EXLA.jit/2`
function for convenience.

`defn` functions are compiled when they are invoked, based on
the type and shapes of the tensors given as arguments. The
compilation is then cached based on the tensors shapes and types.
Calling the same function with a tensor of different values but
same shape and type means no recompilation is performed.

For those interested in writing custom compilers, see `Nx.Defn.Compiler`.

## Invoking custom Elixir code

Inside `defn` you can only call other `defn` functions and
the functions in the `Nx` module. However, it is possible
to use transforms, defined with either `deftransform` or
`deftransformp` to invoke any Elixir code.

You can call code which was defined with `deftransform` from another module:

defmodule MyRemoteModule do
import Nx.Defn

deftransform remote_elixir_code(value) do
IO.inspect(value)
end
end

res = a * b + c
MyRemoteModule.remote_elixir_code(res)
end

You can also define and call a private transform defined through `deftransformp`:

res = a * b + c
custom_elixir_code(res)
end

deftransformp custom_elixir_code(value), do: IO.inspect(value)

For example, the two code snippets invoke `IO.inspect/1`, which is
not a `defn` function, with the value of `res`. This is useful
as it allows developers to transform `defn` code to optimize,
add new properties, and so on.

The only difference between using `deftransform` and `deftransformp` is
wether you want to expose and share the code with other modules, just
like `def` and `defp`.

Transforms can also be used to manipulate Elixir data structures,
such as options. `defn` expects all inputs to be tensors, with the
exception of a default argument (declared with `\\`) which will be
treated as options.

For example, imagine you want to support options where the :axis
key is required. While you can't invoke `Keyword` directly, you
can do it via a transform:

defn sum_axis(t, opts \\ []) do
opts = keyword!(opts, [:axis])
axis = get_axis(opts)
Nx.sum(t, axes: [axis])
end

deftransformp get_axis(opts), do: Keyword.fetch!(opts, :axis)

## Inputs and outputs types

`Nx` and `defn` expect the arguments to be numbers, tensors,
or one composite data type that implements `Nx.LazyContainer`.
Tuples and maps implement `Nx.LazyContainer` by default.
As previously described, `defn` are cached based on the shape,
type, and names of the input tensors, but not their values.

`defn` also accepts two special arguments: functions (or tuples
of functions) and lists (most commonly as keyword lists). Those
values are passed as is to numerical definitions and cached as
a whole. For this reason, you must never capture tensors in
functions or pass tensors in keyword lists.

When numbers are given as arguments, they are always immediately
converted to tensors on invocation. If you want to keep numbers
as is or if you want to pass any other value to numerical definitions,
they must be given as keyword lists.

### Default arguments

`defn` functions support default arguments. They are typically used
as options. For example, imagine you want to create a function named
zeros, which returns a tensor of zeroes with a given type and shape.
It could be implemented like this:

defn zeros(opts \\ []) do
opts = keyword!(opts, type: {:f, 32}, shape: {})
end

The function above accepts `opts` which are then validated and given
default values via the `keyword!/2` function. Note that while it is
possible to access options via the `Access` syntax, such as `opts[:shape]`,
it is not possible to directly call functions in the `Keyword` module
inside `defn`. To freely manipulate any Elixir value inside `defn`,
you have to use transforms, as described in the "Invoking custom Elixir
code" section.

> **Important!** When it comes to JIT compilation, each different set of
> options (as well as anonymous functions) will lead to a different
> compilation of the numerical function.
>
> Furthermore, if tensors are given through keyword lists, they won't
> be cached effectively. Tensors in `defn` are cached based on their shape
> and type, not their value, but this is not true if the tensor is given
> via a default argument or captured by an anonymous function. For this
> reason, it is **extremely discouraged to pass tensors through anonymous
> functions and default arguments**.

### Working with maps and structs

While `Nx` supports maps in `defn`, you must be careful if your numerical
definitions are receiving maps and returning maps. For example, imagine
this code:

defn update_a(map) do
end

The following code increments the value under the key `:a`
by 1. However, because the function receives the whole map and
returns the whole map, it means if the map has 120 keys, the
whole map will be copied to the CPU/GPU, and then brought back.

However, if you do this instead:

defn update_a(map) do
end

And then update the map on Elixir, outside of `defn`:

%{map | a: update_a(map)}

`Nx` will only send the parts of the map that matters.
"""

@compiler_key {Nx.Defn, :default_compiler}
@app_key :default_defn_options

@doc """
Sets the default options for `defn` in the current process.

The options defined here apply to all future invocations of
`defn` done by the current process. It also applies to calls
to the `jit/3` and `stream/3` functions in this module.

The default options are stored only in the process dictionary
and override any global options. This means if you start a
separate process, such as `Task`, the default options must be
set on the new process too.

This function is mostly used for scripting and testing. In your
applications, you typically set the default options in your
config files:

config :nx, :#{@app_key}, [compiler: EXLA, client: :cuda]

"""
def default_options(options) when is_list(options) do
Process.put(@compiler_key, options) || Application.fetch_env!(:nx, @app_key)
end

@doc """
Sets the default options globally.

The options defined here apply to all future invocations of
`defn`. It also applies to calls to the `jit/3` and `stream/3`
functions in this module.

You must avoid calling this function at runtime. It is mostly
useful during scripts or code notebooks to set a default.
If you need to configure a global default options in your
applications, you can do so in your `config/*.exs` files:

config :nx, :#{@app_key}, [compiler: EXLA, client: :cuda]

"""
def global_default_options(options) when is_list(options) do
current = Application.fetch_env!(:nx, @app_key)
Application.put_env(:nx, @app_key, options)
current
end

@doc """
Gets the default options for the current process.
"""
def default_options() do
Process.get(@compiler_key) || Application.fetch_env!(:nx, @app_key)
end

@doc """
Compiles the given anonymous function with the given tensor shapes.

While `jit/2` compiles a function just-in time based on the
input shapes, this function precompiles the given anonymous
function based on the input shapes. This can be beneficial for
large numerical definitions, where the cache mechanism in `jit/2`
may take miliseconds.

For example, take the following definition:

defn softmax(t), do: Nx.exp(t) / Nx.sum(Nx.exp(t))

You can jit and then apply it as:

fun = Nx.Defn.compile(&softmax/1, [Nx.template({3}, {:s, 64})], compiler: EXLA)
fun.(Nx.tensor([1, 2, 3]))

You can also pass a mixture of templates and options when
compiling a function. In such cases, you must only pass
the inputs when invoking the compiled function, as the options
will already be embedded in its compiled value:

fun = Nx.Defn.compile(&Nx.sum/2, [Nx.template({2, 2}, {:s, 64}), [axes: [1]]])
fun.(Nx.iota({2, 2}))

If the input tensors do not match the shape of the tensors
given on compilation, it will raise.

## Options

* `:compiler` - the compiler for the JIT compilation

* `:hooks` - a map of hooks to execute. See `Nx.Defn.Kernel.hook/3`

"""
def compile(fun, template_args, opts \\ [])
when is_function(fun) and is_list(template_args) and is_list(opts) do
{fun, params, _flatten} = Nx.Defn.Compiler.to_lazy_params(fun, template_args)
opts = prepare_options(opts)
compiled_fun = Nx.Defn.Compiler.__compile__(fun, params, opts)

wrap(fun, fn args ->
if Nx.Defn.Compiler.current() do
raise "cannot invoke compiled function when there is a JIT compilation happening"
end

{templates, flatten} = Nx.Defn.Compiler.to_lazy_template(args)
assert_compatible!(templates, params, 1)
[res] = compiled_fun.([flatten])
res
end)
end

defp assert_compatible!([arg | args], [template | templates], pos) do
if Nx.compatible?(arg, template) do
assert_compatible!(args, templates, pos + 1)
else
raise ArgumentError, """
argument at position #{pos} is not compatible with compiled function template.

Template:

#{inspect(template)}

Argument:

#{inspect(arg)}

"""
end
end

defp assert_compatible!([], [], _pos), do: :ok

@doc """
Wraps an anonymous function with just-in-time compilation.

Once invoked, the wrapped anonymous function will perform just
in time compilation with the configured compiler. For example,
take the following definition:

defn softmax(t), do: Nx.exp(t) / Nx.sum(Nx.exp(t))

You can jit and then apply it as:

fun = Nx.Defn.jit(&softmax/1, compiler: EXLA)
fun.(Nx.tensor([1, 2, 3]))

## Options

* `:compiler` - the compiler for the JIT compilation

* `:hooks` - a map of hooks to execute. See `Nx.Defn.Kernel.hook/3`

* `:on_conflict` - what to do if a JIT compilation is already in place.
It may be `:raise` (the default), `:force` (forces a new JIT compilation),
or `:reuse` (reuses the exiting JIT compilation). It is not recommended
to set the `:compiler` option when reusing.

"""
def jit(fun, opts \\ []) when is_function(fun) and is_list(opts) do
if Keyword.keyword?(opts) do
wrap(fun, &jit_apply(fun, &1, opts))
else
IO.warn("jit/3 is deprecated, use jit/2 instead")
jit_apply(fun, opts, [])
end
end

def jit(fun, args, opts) when is_function(fun) and is_list(args) and is_list(opts) do
jit_apply(fun, args, opts)
end

@doc """
Invokes the anonymous function with just-in-time compilation.

This function is equivalent to calling `jit/2` and then applying
the given arguments to the anonymous function.

For example, take the following definition:

defn softmax(t), do: Nx.exp(t) / Nx.sum(Nx.exp(t))

You can `jit_apply/3` it as:

Nx.Defn.jit_apply(&softmax/1, [Nx.tensor([1, 2, 3])], compiler: EXLA)

It accepts the same options as `jit/2`.
"""
def jit_apply(fun, args, opts \\ [])
when is_function(fun) and is_list(args) and is_list(opts) do
{on_conflict, opts} = Keyword.pop(opts, :on_conflict, :raise)

cond do
Nx.Defn.Compiler.current() == nil ->
do_jit_apply(fun, args, opts)

on_conflict == :raise ->
raise "cannot invoke JITed function when there is a JIT compilation happening"

on_conflict == :force ->
do_jit_apply(fun, args, opts)

on_conflict == :reuse ->
apply(fun, args)
end
end

defp do_jit_apply(fun, args, opts) do
opts = prepare_options(opts)
{fun, params, flatten} = Nx.Defn.Compiler.to_lazy_params(fun, args)
[res] = Nx.Defn.Compiler.__jit__(fun, params, [flatten], opts)
res
end

@deprecated "Use jit/2 or jit_apply/3 with the :on_conflict option"
def jit_or_apply(fun, args, opts \\ [])
when is_function(fun) and is_list(args) and is_list(opts) do
if Nx.Defn.Compiler.current() do
apply(fun, args)
else
jit(fun, args, opts)
end
end

@doc """
Wraps an anonymous function to return its underlying defn expression.

> #### Warning {: .warning}
>
> This function must be invoked for debugging purposes only.

## Options

* `:hooks` - a map of hooks to execute. See `Nx.Defn.Kernel.hook/3`

"""
def debug_expr(fun, opts \\ []) when is_function(fun) and is_list(opts) do
wrap(fun, &debug_expr_apply(fun, &1, opts))
end

@doc """
Invokes the anonymous function to return its underlying defn expression.

> #### Warning {: .warning}
>
> This function must be invoked for debugging purposes only.

It accepts the same options as `debug_expr/2`.
"""
def debug_expr_apply(fun, args, opts \\ []) when is_function(fun) and is_list(args) do
opts = opts |> prepare_options() |> Keyword.put(:compiler, Nx.Defn.Debug)
{fun, params, flatten} = Nx.Defn.Compiler.to_lazy_params(fun, args)
[res] = Nx.Defn.Compiler.__jit__(fun, params, [flatten], opts)
res
end

@doc """
Starts streaming the given anonymous function with just-in-time
compilation.

At least two arguments are expected:

1. The first argument is a tensor template of the data to
be streamed in

2. The second argument is a tensor with the stream initial state

The streaming function must return a two element tuple, the
first element is the data to be sent and the second is the
accumulator.

For each streamed chunk, you must call `Nx.Stream.send/2` and
`Nx.Stream.recv/1`. You don't need to call `recv` immediately
after `send`, but doing so can be a useful mechanism to provide
backpressure. Once all chunks are sent, you must use `Nx.Stream.done/1`
to receive the accumulated result. Let's see an example:

defmodule Streamed do
import Nx.Defn

defn sum(tensor, acc) do
{acc, tensor + acc}
end
end

Now let's invoke it:

stream = Nx.Defn.stream(&Streamed.sum/2, [Nx.template({}, {:s, 64}), 0])

for i <- 1..5 do
Nx.Stream.send(stream, i)
IO.inspect {:chunk, Nx.Stream.recv(stream)}
end

IO.inspect {:result, Nx.Stream.done(stream)}

It will print:

{:chunk, 0}
{:chunk, 1}
{:chunk, 2}
{:chunk, 3}
{:chunk, 4}
{:result, 5}

## Options

* `:hooks` - a map of hooks to execute. See `Nx.Defn.Kernel.hook/3`

"""
def stream(fun, args, opts \\ [])
when is_function(fun) and is_list(args) and is_list(opts) do
if Nx.Defn.Compiler.current() do
raise "cannot call Nx.Defn.stream/3 when there is a JIT compilation happening"
end

opts = prepare_options(opts)
{fun, params, flatten} = Nx.Defn.Compiler.to_lazy_params(fun, args)

case args do
[_input, acc | _] ->
acc = Nx.Defn.Composite.traverse(acc, &Nx.to_tensor/1)
[stream] = Nx.Defn.Compiler.__stream__(fun, hd(params), acc, params, [flatten], opts)
stream

_ ->
raise ArgumentError, "Nx.Defn.stream/3 expects at least two arguments"
end
end

defp prepare_options(opts) do
opts = Keyword.merge(default_options(), opts)

if not is_map(Keyword.get(opts, :hooks, %{})) do
raise ArgumentError, ":hooks option must be a map"
end

opts
end

defp wrap(fun, callback) do
{:arity, arity} = Function.info(fun, :arity)
Nx.Defn.Compiler.fun(arity, callback)
end

@doc """
Receives an anonymous function and returns a new anonymous function
that returns the gradient of the input function when invoked.

## Examples

iex> fun = Nx.Defn.grad(fn x -> Nx.sin(x) end)
iex> fun.(Nx.tensor(0))
#Nx.Tensor<
f32
1.0
>

"""
def grad(fun) when is_function(fun, 1) do
fn t -> grad(t, fun) end
end

@doc """
Computes the gradient of the given `var` on `fun`.

The result of the `grad` function must be a scalar tensor.
If a non-scalar tensor is given, it is assumed the additional
dimensions are batch dimensions.

### Examples

end

To differentiate on multiple vars, pass a tuple as first argument:

grad({a, b}, fn {a, b} -> Nx.tanh(a) + Nx.power(b, 2) end)
end

`var_or_vars` can be any `Nx.Container` with one or multiple
tensors.
"""
def grad(var_or_vars, fun) when is_function(fun, 1) do
jit_apply(
fn var_or_vars ->
end,
[var_or_vars],
on_conflict: :reuse
)
end

@doc """
Receives an anonymous function and returns a new anonymous function
that returns the value and gradient of the input function when invoked.

## Examples

iex> fun = Nx.Defn.value_and_grad(fn x -> Nx.sin(x) end)
iex> value
#Nx.Tensor<
f32
0.0
>
#Nx.Tensor<
f32
1.0
>

"""
def value_and_grad(fun) when is_function(fun, 1) do
fn t -> value_and_grad(t, fun) end
end

@doc """
Computes the value and gradient of the given `var` on `fun`
with an optional data transformation.

It returns a tuple with the value and the gradient.

### Examples

end

To differentiate on multiple vars, pass a tuple as first argument:

value_and_grad({a, b}, fn {a, b} -> Nx.tanh(a) + Nx.power(b, 2) end)
end

`var_or_vars` can be any `Nx.Container` with one or multiple
tensors.

`transform` allows you to transform the expression before the gradient is
calculated. This enables optimizations that reuse parts of expressions. As
an example, consider the following objective function:

defn objective(predict_fn, loss_fn, params, inputs, targets) do
preds = predict_fn.(params, inputs)
loss = loss_fn.(preds, targets)
{preds, loss}
end

You can compute the gradient with respect to just the loss function by applying
a transform:

{{preds, loss}, gradient} = value_and_grad(params, &objective(predict_fn, loss_fn, &1, inputs, targets), &elem(&1, 1))

`preds` can be re-used to compute other metrics such as accuracy, absolute error,
etc. without having to do another forward pass.
"""
def value_and_grad(var_or_vars, fun, transform \\ & &1)
when Kernel.and(is_function(fun, 1), is_function(transform, 1)) do
jit_apply(
fn var_or_vars -> Nx.Defn.Grad.transform(var_or_vars, fun, transform) end,
[var_or_vars],
on_conflict: :reuse
)
end

@doc """
Defines a public numerical function.
"""
defmacro defn(call, do: block) do
define_defn(:def, call, block, __CALLER__)
end

@doc """
Defines a private numerical function.

Private numerical functions are always inlined by
their callers at compilation time. This happens to
all local function calls within `defn`.
"""
defmacro defnp(call, do: block) do
define_defn(:defp, call, block, __CALLER__)
end

@doc """
Can be used to define bodiless clauses for multi-clause transforms.

## Examples

deftransform foo(bar, baz \\ 1)
deftransform foo(bar, 1), do: bar
deftransform foo(bar, baz), do: bar + baz
"""
defmacro deftransform(call) do
define_transform(:def, call, nil, __CALLER__)
end

@doc """
Defines a transform that executes the given `fun` with `arg`
when building `defn` expressions.

## Example

Take the following defn expression:

defn tanh_power(a, b) do
Nx.tanh(a) + Nx.power(b, 2)
end

Let's see a trivial example, which is to use `IO.inspect/1` to
print a tensor expression at definition time:

defn tanh_power(a, b) do
Nx.tanh(a) + Nx.power(b, 2) |> my_inspect()
end

deftransformp my_inspect(expr), do: IO.inspect(expr)

Or:

defn tanh_power(a, b) do
res = Nx.tanh(a) + Nx.power(b, 2)
my_inspect(res)
res
end

When invoked in both cases, it will print the expression being built
by `defn`:

#Nx.Defn.Expr<
parameter a
parameter c
b = tanh [ a ] ()
d = power [ c, 2 ] ()
e = add [ b, d ] ()
>

Although, for convenience, you might use `print_expr/2` instead.
"""
defmacro deftransform(call, do: block) do
define_transform(:def, call, block, __CALLER__)
end

@doc """
Private function version for `deftransform/1`
"""
defmacro deftransformp(call) do
define_transform(:defp, call, nil, __CALLER__)
end

@doc """
Private function version for `deftransform/2`
"""
defmacro deftransformp(call, do: block) do
define_transform(:defp, call, block, __CALLER__)
end

## Callbacks

defp define_defn(kind, call, block, env) do
assert_no_guards!(kind, call, env)
# Note name here is not necessarily an atom due to unquote(name) support
{name, args} = decompose_call!(kind, call, env)
arity = length(args)

defaults =
for {{:\\, meta, [_, default]}, i} <- Enum.with_index(args),
do: {i, {meta, Macro.escape(default)}},
into: []

quote do
unquote(__MODULE__).__define__(
__MODULE__,
unquote(kind),
unquote(name),
unquote(arity),
:numerical,
%{unquote_splicing(defaults)}
)

unquote(kind)(unquote(call)) do
use Nx.Defn.Kernel
unquote(block)
end
end
end

defp define_transform(kind, call, block, env) do
# Note name here is not necessarily an atom due to unquote(name) support
{name, args} = decompose_call!(kind, call, env)
arity = length(args)

defaults =
for {{:\\, meta, [_, default]}, i} <- Enum.with_index(args),
do: {i, {meta, Macro.escape(default)}},
into: []

define_ast =
quote do
unquote(__MODULE__).__define__(
__MODULE__,
unquote(kind),
unquote(name),
unquote(arity),
:transform,
%{unquote_splicing(defaults)}
)
end

def_ast =
if block do
quote do
Kernel.unquote(kind)(unquote(call), do: unquote(block))
end
else
quote do
Kernel.unquote(kind)(unquote(call))
end
end

{:__block__, [], [define_ast, def_ast]}
end

defp decompose_call!(kind, {:when, _, [call, _guards]}, env),
do: decompose_call!(kind, call, env)

defp decompose_call!(_kind, {{:unquote, _, [name]}, _, args}, _env) do
{name, args}
end

defp decompose_call!(kind, call, env) do
case Macro.decompose_call(call) do
{name, args} ->
{name, args}

:error ->
compile_error!(
env,
"first argument of #{kind}n must be a call, got: #{Macro.to_string(call)}"
)
end
end

defp assert_no_guards!(kind, {:when, _, _}, env) do
compile_error!(env, "guards are not supported by #{kind}n")
end

defp assert_no_guards!(_kind, _call, _env), do: :ok

# Internal attributes
@defn_exports_key :__defn_exports__

@doc false
def __define__(module, kind, name, arity, type, defaults) do
exports =
if exports = Module.get_attribute(module, @defn_exports_key) do
exports
else
Module.put_attribute(module, :before_compile, __MODULE__)
%{}
end

current_export = %{
type: type,
kind: kind,
defaults: defaults
}

exports =
if type == :transform do
# This will ensure that we capture the defaults for a bodiless head
# while keeping the definitions properly for the same arity
Map.update(exports, {name, arity}, current_export, fn item ->
%{
type: item.type || current_export.type,
kind: item.kind || current_export.kind,
defaults: if(item.defaults == [], do: current_export.defaults, else: item.defaults)
}
end)
else
Map.put(exports, {name, arity}, current_export)
end

Module.put_attribute(module, @defn_exports_key, exports)
:ok
end

defp compile_error!(env, description) do
raise CompileError, line: env.line, file: env.file, description: description
end

@doc false
defmacro __before_compile__(env) do
defn_exports = Module.get_attribute(env.module, @defn_exports_key)
Nx.Defn.Compiler.__compile__(env, defn_exports)
end
end
``````