class_docstr: Implements the Numpy backend ops for a PC auto-batching VM.
class_docstr_tok: ["Implements","the","Numpy","backend","ops","for","a","PC","auto-batching","VM","."]
docstr: Backend hook for preparing Tensors for `cond`.
  
  Does nothing in the numpy backend (needed by the TensorFlow backend).
  
  Args:
    state: A state to be prepared for use in conditionals.
  
  Returns:
    state: The prepared state.
docstr_tok: ["Backend","hook","for","preparing","Tensors","for","`","cond","`",".","Does","nothing","in","the","numpy","backend","(","needed","by","the","TensorFlow","backend",")",".","Args",":","state",":","A","state","to","be","prepared","for","use","in","conditionals",".","Returns",":","state",":","The","prepared","state","."]
code: def prepare_for_cond(self, state):
      """Backend hook for preparing Tensors for `cond`.
  
      Does nothing in the numpy backend (needed by the TensorFlow backend).
  
      Args:
        state: A state to be prepared for use in conditionals.
  
      Returns:
        state: The prepared state.
      """
      return state
  
imports: import collections
  import numpy
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 14 for calling the NumpyBackend obj's underlying member method code with example usage: obj.prepare_for_cond(state) and returns: state