class_docstr: An auto-batching DSL context.
  
  Auto-batching DSL operations are methods on the `ProgramBuilder` object.  It's
  used like this:
  
  ```python
  ab = dsl.ProgramBuilder()
  
  def fib_type(arg_types):
    return arg_types[0]
  
  with ab.function(type_inference=fib_type) as fibonacci:
    n = ab.param('n')
    ab.var.cond = ab.primop(lambda n: n > 1)
    with ab.if_(ab.var.cond):
      ab.var.nm1 = ab.primop(lambda n: n - 1)
      ab.var.fibm1 = ab.call(fibonacci, [ab.var.nm1])
      ab.var.nm2 = ab.primop(lambda n: n - 2)
      ab.var.fibm2 = ab.call(fibonacci, [ab.var.nm2])
      ab.var.ans = ab.primop(lambda fibm1, fibm2: fibm1 + fibm2)
    with ab.else_():
      ab.var.ans = ab.const(1)
    ab.return_(ab.var.ans)
  
  prog = ab.program(main=fibonacci)
  # Now `prog` is a well-formed `instructions.Program`, and the context
  # `ab` is no longer needed.
  ```
  
  Note the sequence of method calls on `ProgramBuilder` corresponds to the
  source code of the `Program` being defined, not its runtime behavior.  This is
  because (a) functions are defined with a context manager (rather than a Python
  function) which executes its body immediately and exactly once; and (b)
  function call instructions (and primitive operations) are just recorded, not
  entered recursively.
docstr: Forward-declares a function to be defined later with `define_function`.
  
  This useful for defining mutually recursive functions:
  ```python
  ab = dsl.ProgramBuilder()
  
  foo = ab.declare_function(...)
  
  with ab.function(...) as bar:
    ...
    ab.call(foo)
  
  with ab.define_function(foo):
    ...
    ab.call(bar)
  ```
  
  It is an error to call but never define a declared function.
  
  Args:
    name: Optional string naming this function when the program is printed.
    type_inference: A Python callable giving the type signature of the
      function being defined.  See `function`.
  
  Returns:
    function: An `instructions.Function` object representing the function
      being declared.  It can be passed to `call` to call it, and to
      `define_function` to define it.
code: def declare_function(self, name=None, type_inference=None):
      """Forward-declares a function to be defined later with `define_function`.
  
      This useful for defining mutually recursive functions:
      ```python
      ab = dsl.ProgramBuilder()
  
      foo = ab.declare_function(...)
  
      with ab.function(...) as bar:
        ...
        ab.call(foo)
  
      with ab.define_function(foo):
        ...
        ab.call(bar)
      ```
  
      It is an error to call but never define a declared function.
  
      Args:
        name: Optional string naming this function when the program is printed.
        type_inference: A Python callable giving the type signature of the
          function being defined.  See `function`.
  
      Returns:
        function: An `instructions.Function` object representing the function
          being declared.  It can be passed to `call` to call it, and to
          `define_function` to define it.
      """
      return inst.Function(
          graph=None,
          vars_in=[],
          vars_out=[],
          type_inference=type_inference,
          name=name,
      )
  
imports: import collections
  import contextlib
  import inspect
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 18 for calling the ProgramBuilder obj's underlying member method code with example usage: obj.declare_function(name, type_inference) and returns: inst