class_docstr: Base class for Deterministic distributions.
class_docstr_tok: ["Base","class","for","Deterministic","distributions","."]
docstr: The bijector maps a zero-dimensional null Tensor input to `self.loc`.
docstr_tok: ["The","bijector","maps","a","zero-dimensional","null","Tensor","input","to","`","self.loc","`","."]
code: def _default_event_space_bijector(self):
      """The bijector maps a zero-dimensional null Tensor input to `self.loc`."""
      # The shape of the pulled back null tensor will be `self.loc.shape + (0,)`.
      # First we pad to a tensor of zeros with shape `self.loc.shape + (1,)`.
      pad_zero = pad.Pad([(1, 0)])
      # Next, we squeeze to a tensor of zeros with shape matching `self.loc`.
      zeros_squeezed = reshape.Reshape([], event_shape_in=[1])(pad_zero)
      # Finally, we shift the zeros by `self.loc`.
      return shift.Shift(self.loc)(zeros_squeezed)
  
imports: import abc
  import six
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import pad
  from tensorflow_probability.python.bijectors import reshape
  from tensorflow_probability.python.bijectors import shift
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 11 for calling the _BaseDeterministic obj's underlying member method code with example usage: obj._default_event_space_bijector() and returns: shift