def __init__(self,<EOL>loc,<EOL>atol=None,<EOL>rtol=None,<EOL>validate_args=False,<EOL>allow_nan_stats=True,<EOL>name="<STR_LIT>"):
parameters = dict(locals())<EOL>super(VectorDeterministic, self).__init__(<EOL>loc,<EOL>atol=atol,<EOL>rtol=rtol,<EOL>is_vector=True,<EOL>validate_args=validate_args,<EOL>allow_nan_stats=allow_nan_stats,<EOL>parameters=parameters,<EOL>name=name)<EOL>
Initialize a `VectorDeterministic` distribution on `R^k`, for `k >= 0`.

        Note that there is only one point in `R^0`, the "point" `[]`.  So if `k = 0`
        then `self.prob([]) == 1`.

        The `atol` and `rtol` parameters allow for some slack in `pmf`
        computations, e.g. due to floating-point error.

        ```
        pmf(x; loc)
          = 1, if All[Abs(x - loc) <= atol + rtol * Abs(loc)],
          = 0, otherwise
        ```

        Args:
          loc: Numeric `Tensor` of shape `[B1, ..., Bb, k]`, with `b >= 0`, `k >= 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.