tensorflow_probability/python/distributions/internal/slicing.py
def _apply_single_step(dist, params_event_ndims, slices, params_overrides):
  """Applies a single slicing step to `dist`, returning a new instance."""
  if len(slices) == 1 and slices[0] == Ellipsis:
    # The path used by Distribution.copy: batch_slice(...args..., Ellipsis)
    override_dict = {}
  else:
    override_dict = _slice_params_to_dict(dist, params_event_ndims, slices)
  override_dict.update(params_overrides)
  parameters = dict(dist.parameters, **override_dict)
  new_dist = type(dist)(**parameters)
  return new_dist
def _apply_single_step(dist, params_event_ndims, slices, params_overrides):
  if len(slices) == 1 and slices[0] == Ellipsis:
    # The path used by Distribution.copy: batch_slice(...args..., Ellipsis)
    override_dict = {}
  else:
    override_dict = _slice_params_to_dict(dist, params_event_ndims, slices)
  override_dict.update(params_overrides)
  parameters = dict(dist.parameters, **override_dict)
  new_dist = type(dist)(**parameters)
  return new_dist
def _apply_single_step ( dist , params_event_ndims , slices , params_overrides ) : if len ( slices ) == 1 and slices [ 0 ] == Ellipsis : # The path used by Distribution.copy: batch_slice(...args..., Ellipsis) override_dict = { } else : override_dict = _slice_params_to_dict ( dist , params_event_ndims , slices ) override_dict . update ( params_overrides ) parameters = dict ( dist . parameters , * * override_dict ) new_dist = type ( dist ) ( * * parameters ) return new_dist
Applies a single slicing step to `dist`, returning a new instance.
Applies a single slicing step to dist returning a new instance .