class_docstr: Context object for auto-batching multiple Python functions together.
  
  Warning: This is alpha-grade software.  The exact subset of Python that can be
  successfully auto-batched is ill-specified and subject to change.
  Furthermore, the errors elicited by stepping outside this subset are likely to
  be confusing and misleading.  Use at your own risk.
  
  Usage:
  ```python
  ctx = frontend.Context()
  
  @ctx.batch(type_inference=lambda ...)
  def my_single_example_function_1(args):
    ...
  
  @ctx.batch(type_inference=lambda ...)
  def my_single_example_function_2(args):
    ...
  
  # etc
  ```
  
  Then calling any of the decorated functions will execute a batch computation.
  The decorated functions may call each other, including mutually recursively.
  See also the `batch` method.
  
  Limitations:
  - You must explicitly decorate every function to be auto-batched.
  - All calls to them must call them by name (no higher-order auto-batching).
  - Auto-batched functions must be defined with `def`, not `lambda`.
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docstr: Helper for calling program_lowered that computes the type signature.
code: def lowered_for_args(self, name, args, backend):
      """Helper for calling program_lowered that computes the type signature."""
      sig = ab_type_inference.signature(
          self.program(main=name), args, backend
      )
      lowered = self.program_lowered(
          main=name, sig=sig, backend=backend
      )
      return lowered
  
imports: import contextlib
  import functools
  import gast
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.auto_batching import allocation_strategy
  from tensorflow_probability.python.experimental.auto_batching import dsl
  from tensorflow_probability.python.experimental.auto_batching import gast_util
  from tensorflow_probability.python.experimental.auto_batching import instructions
  from tensorflow_probability.python.experimental.auto_batching import lowering
  from tensorflow_probability.python.experimental.auto_batching import stack_optimization
  from tensorflow_probability.python.experimental.auto_batching import stackless
  from tensorflow_probability.python.experimental.auto_batching import tf_backend
  from tensorflow_probability.python.experimental.auto_batching import type_inference
  from tensorflow_probability.python.experimental.auto_batching import virtual_machine
  from tensorflow.python.autograph.converters import break_statements
  from tensorflow.python.autograph.converters import continue_statements
  from tensorflow.python.autograph.converters import return_statements
  from tensorflow.python.autograph.core import converter
  from tensorflow.python.autograph.pyct import anno
  from tensorflow.python.autograph.pyct import inspect_utils
  from tensorflow.python.autograph.pyct import parser
  from tensorflow.python.autograph.pyct import qual_names
  from tensorflow.python.autograph.pyct import templates
  from tensorflow.python.autograph.pyct import transformer
  from tensorflow.python.autograph.pyct.common_transformers import anf
  from tensorflow.python.autograph.pyct.static_analysis import activity
label_desc: Use image node_id 11 for calling the Context obj's underlying member method code with example usage: obj.lowered_for_args(name, args, backend) and returns: lowered