def __init__(self,<EOL>loc=None,<EOL>scale=None,<EOL>validate_args=False,<EOL>allow_nan_stats=True,<EOL>name="<STR_LIT>"):
parameters = dict(locals())<EOL>if scale is None:<EOL><INDENT>raise ValueError("<STR_LIT>")<EOL><DEDENT>if not dtype_util.is_floating(scale.dtype):<EOL><INDENT>raise TypeError("<STR_LIT>")<EOL><DEDENT>with tf.name_scope(name) as name:<EOL><INDENT>loc = loc if loc is None else tf.convert_to_tensor(<EOL>value=loc, name="<STR_LIT>", dtype=scale.dtype)<EOL>batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(<EOL>loc, scale)<EOL><DEDENT>super(MultivariateNormalLinearOperator, self).__init__(<EOL>distribution=normal.Normal(<EOL>loc=tf.zeros([], dtype=scale.dtype),<EOL>scale=tf.ones([], dtype=scale.dtype)),<EOL>bijector=affine_linear_operator_bijector.AffineLinearOperator(<EOL>shift=loc, scale=scale, validate_args=validate_args),<EOL>batch_shape=batch_shape,<EOL>event_shape=event_shape,<EOL>validate_args=validate_args,<EOL>name=name)<EOL>self._parameters = parameters<EOL>
Construct Multivariate Normal distribution on `R^k`.

        The `batch_shape` is the broadcast shape between `loc` and `scale`
        arguments.

        The `event_shape` is given by last dimension of the matrix implied by
        `scale`. The last dimension of `loc` (if provided) must broadcast with this.

        Recall that `covariance = scale @ scale.T`.

        Additional leading dimensions (if any) will index batches.

        Args:
          loc: Floating-point `Tensor`. If this is set to `None`, `loc` is
            implicitly `0`. When specified, may have shape `[B1, ..., Bb, k]` where
            `b >= 0` and `k` is the event size.
          scale: Instance of `LinearOperator` with same `dtype` as `loc` and shape
            `[B1, ..., Bb, k, k]`.
          validate_args: Python `bool`, default `False`. Whether to validate input
            with asserts. If `validate_args` is `False`, and the inputs are
            invalid, correct behavior is not guaranteed.
          allow_nan_stats: Python `bool`, default `True`. If `False`, raise an
            exception if a statistic (e.g. mean/mode/etc...) is undefined for any
            batch member If `True`, batch members with valid parameters leading to
            undefined statistics will return NaN for this statistic.
          name: The name to give Ops created by the initializer.

        Raises:
          ValueError: if `scale` is unspecified.
          TypeError: if not `scale.dtype.is_floating`