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: Broadcasts val to the shape of target.
  
  Args:
    val: Python or Numpy array to be broadcast. Must be `np.array` compatible
      and broadcast-compatible with `target`.
    target: Python or Numpy array whose shape we broadcast `val` to match.
    name: Optional name for the op.
  
  Returns:
    broadcast_val: A `np.ndarray` with shape matching `val + target`. Provided
      that `val`'s dimension sizes are all smaller or equal to `target`'s, the
      returned value will be the shape of `target`.
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code: def broadcast_to_shape_of(self, val, target, name=None):
      """Broadcasts val to the shape of target.
  
      Args:
        val: Python or Numpy array to be broadcast. Must be `np.array` compatible
          and broadcast-compatible with `target`.
        target: Python or Numpy array whose shape we broadcast `val` to match.
        name: Optional name for the op.
  
      Returns:
        broadcast_val: A `np.ndarray` with shape matching `val + target`. Provided
          that `val`'s dimension sizes are all smaller or equal to `target`'s, the
          returned value will be the shape of `target`.
      """
      del name
      val = np.array(val)
      return val + np.zeros_like(target, dtype=val.dtype)
  
imports: import collections
  import numpy
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 12 for calling the NumpyBackend obj's underlying member method code with example usage: obj.broadcast_to_shape_of(val, target, name) and returns: unknown