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: Implements a where selector for the Numpy backend.
  
  Extends `tf.where` to support broadcasting of `on_false`.
  
  Args:
    condition: A `bool` `np.ndarray`, either a vector having length
      `y.shape[0]` or matching the full shape of `y`.
    x: `np.ndarray` of values to take when `condition` is `True`.
    y: `np.ndarray` of values to take when `condition` is `False`. May
      be smaller than `x`, as long as it is broadcast-compatible.
    name: Optional name for the op.
  
  Returns:
    masked: A `np.ndarray` where indices corresponding to `True` values in
      `condition` come from the corresponding value in `x`, and others come
      from `y`.
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code: def where(self, condition, x, y, name=None):
      """Implements a where selector for the Numpy backend.
  
      Extends `tf.where` to support broadcasting of `on_false`.
  
      Args:
        condition: A `bool` `np.ndarray`, either a vector having length
          `y.shape[0]` or matching the full shape of `y`.
        x: `np.ndarray` of values to take when `condition` is `True`.
        y: `np.ndarray` of values to take when `condition` is `False`. May
          be smaller than `x`, as long as it is broadcast-compatible.
        name: Optional name for the op.
  
      Returns:
        masked: A `np.ndarray` where indices corresponding to `True` values in
          `condition` come from the corresponding value in `x`, and others come
          from `y`.
      """
      del name
      return _where_leading_dims(condition, x, y)
  
imports: import collections
  import numpy
  from tensorflow_probability.python.experimental.auto_batching import instructions
label_desc: Use image node_id 15 for calling the NumpyBackend obj's underlying member method code with example usage: obj.where(condition, x, y, name) and returns: _where_leading_dims