class_docstr: Implements the TF backend ops for a PC auto-batching VM.
docstr: Construct a new backend instance.
  
  Args:
    safety_checks: Python `bool` indicating whether we should use runtime
      assertions to detect stack overflow/underflow.
    while_parallel_iterations: Python `int`, the argument to pass along to
      `tf.while_loop(..., parallel_iterations=while_parallel_iterations)`
    while_maximum_iterations: Python `int` or None, the argument to pass along
      to `tf.while_loop(..., maximum_iterations=while_maximum_iterations)`
    basic_block_xla_device: Python `str` indicating the device to which basic
      blocks should be targeted (i.e. 'CPU:0' or 'GPU:0'); if not None.
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code: def __init__(
      self,
      safety_checks=True,
      while_parallel_iterations=10,
      while_maximum_iterations=None,
      basic_block_xla_device=None,
  ):
      """Construct a new backend instance.
  
      Args:
        safety_checks: Python `bool` indicating whether we should use runtime
          assertions to detect stack overflow/underflow.
        while_parallel_iterations: Python `int`, the argument to pass along to
          `tf.while_loop(..., parallel_iterations=while_parallel_iterations)`
        while_maximum_iterations: Python `int` or None, the argument to pass along
          to `tf.while_loop(..., maximum_iterations=while_maximum_iterations)`
        basic_block_xla_device: Python `str` indicating the device to which basic
          blocks should be targeted (i.e. 'CPU:0' or 'GPU:0'); if not None.
      """
      self._safety_checks = safety_checks
      self._while_parallel_iterations = while_parallel_iterations
      self._while_maximum_iterations = while_maximum_iterations
      self._basic_block_xla_device = basic_block_xla_device
  
imports: import collections
  import contextlib
  import six
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.experimental.auto_batching import instructions
  from tensorflow_probability.python.experimental.auto_batching import xla
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow.python.ops import control_flow_util
label_desc: Use image node_id 1 to create a new TensorFlowBackend object from inherited base classes: object with example: obj = TensorFlowBackend(safety_checks, while_parallel_iterations, while_maximum_iterations, basic_block_xla_device)