parameters: (self,
                 loc,
                 atol=None,
                 rtol=None,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="Deterministic")
docstring: Initialize a scalar `Deterministic` distribution.
  
      The `atol` and `rtol` parameters allow for some slack in `pmf`, `cdf`
      computations, e.g. due to floating-point error.
  
      ```
      pmf(x; loc)
        = 1, if Abs(x - loc) <= atol + rtol * Abs(loc),
        = 0, otherwise.
      ```
  
      Args:
        loc: Numeric `Tensor` of shape `[B1, ..., Bb]`, with `b >= 0`.
          The point (or batch of points) on which this distribution is supported.
        atol:  Non-negative `Tensor` of same `dtype` as `loc` and broadcastable
          shape.  The absolute tolerance for comparing closeness to `loc`.
          Default is `0`.
        rtol:  Non-negative `Tensor` of same `dtype` as `loc` and broadcastable
          shape.  The relative tolerance for comparing closeness to `loc`.
          Default is `0`.
        validate_args: Python `bool`, default `False`. When `True` distribution
          parameters are checked for validity despite possibly degrading runtime
          performance. When `False` invalid inputs may silently render incorrect
          outputs.
        allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
          (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
          result is undefined. When `False`, an exception is raised if one or
          more of the statistic's batch members are undefined.
        name: Python `str` name prefixed to Ops created by this class.
docstring_summary: Initialize a scalar `Deterministic` distribution.
docstring_tokens: Initialize a scalar Deterministic distribution .
function: def __init__(self,
                 loc,
                 atol=None,
                 rtol=None,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="Deterministic"):
      """Initialize a scalar `Deterministic` distribution.
  
      The `atol` and `rtol` parameters allow for some slack in `pmf`, `cdf`
      computations, e.g. due to floating-point error.
  
      ```
      pmf(x; loc)
        = 1, if Abs(x - loc) <= atol + rtol * Abs(loc),
        = 0, otherwise.
      ```
  
      Args:
        loc: Numeric `Tensor` of shape `[B1, ..., Bb]`, with `b >= 0`.
          The point (or batch of points) on which this distribution is supported.
        atol:  Non-negative `Tensor` of same `dtype` as `loc` and broadcastable
          shape.  The absolute tolerance for comparing closeness to `loc`.
          Default is `0`.
        rtol:  Non-negative `Tensor` of same `dtype` as `loc` and broadcastable
          shape.  The relative tolerance for comparing closeness to `loc`.
          Default is `0`.
        validate_args: Python `bool`, default `False`. When `True` distribution
          parameters are checked for validity despite possibly degrading runtime
          performance. When `False` invalid inputs may silently render incorrect
          outputs.
        allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
          (e.g., mean, mode, variance) use the value "`NaN`" to indicate the
          result is undefined. When `False`, an exception is raised if one or
          more of the statistic's batch members are undefined.
        name: Python `str` name prefixed to Ops created by this class.
      """
      super(Deterministic, self).__init__(
          loc,
          atol=atol,
          rtol=rtol,
          validate_args=validate_args,
          allow_nan_stats=allow_nan_stats,
          name=name)
function_tokens: def __init__ ( self , loc , atol = None , rtol = None , validate_args = False , allow_nan_stats = True , name = "Deterministic" ) : super ( Deterministic , self ) . __init__ ( loc , atol = atol , rtol = rtol , validate_args = validate_args , allow_nan_stats = allow_nan_stats , name = name )