parameters: (self,
                 loc=None,
                 scale=None,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="MultivariateNormalLinearOperator")
docstring: 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`
docstring_summary: Construct Multivariate Normal distribution on `R^k`.
docstring_tokens: Construct Multivariate Normal distribution on R^k .
function: def __init__(self,
                 loc=None,
                 scale=None,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="MultivariateNormalLinearOperator"):
      """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`
      """
      parameters = dict(locals())
      if scale is None:
        raise ValueError("Missing required `scale` parameter.")
      if not scale.dtype.is_floating:
        raise TypeError("`scale` parameter must have floating-point dtype.")
  
      with ops.name_scope(name, values=[loc] + scale.graph_parents) as name:
        # Since expand_dims doesn't preserve constant-ness, we obtain the
        # non-dynamic value if possible.
        loc = ops.convert_to_tensor(loc, name="loc") if loc is not None else loc
        batch_shape, event_shape = distribution_util.shapes_from_loc_and_scale(
            loc, scale)
  
      super(MultivariateNormalLinearOperator, self).__init__(
          distribution=normal.Normal(
              loc=array_ops.zeros([], dtype=scale.dtype),
              scale=array_ops.ones([], dtype=scale.dtype)),
          bijector=AffineLinearOperator(
              shift=loc, scale=scale, validate_args=validate_args),
          batch_shape=batch_shape,
          event_shape=event_shape,
          validate_args=validate_args,
          name=name)
      self._parameters = parameters
function_tokens: def __init__ ( self , loc = None , scale = None , validate_args = False , allow_nan_stats = True , name = "MultivariateNormalLinearOperator" ) : parameters = dict ( locals ( ) ) if scale is None : raise ValueError ( "Missing required `scale` parameter." ) if not scale . dtype . is_floating : raise TypeError ( "`scale` parameter must have floating-point dtype." ) with ops . name_scope ( name , values = [ loc ] + scale . graph_parents ) as name : # Since expand_dims doesn't preserve constant-ness, we obtain the # non-dynamic value if possible. loc = ops . convert_to_tensor ( loc , name = "loc" ) if loc is not None else loc batch_shape , event_shape = distribution_util . shapes_from_loc_and_scale ( loc , scale ) super ( MultivariateNormalLinearOperator , self ) . __init__ ( distribution = normal . Normal ( loc = array_ops . zeros ( [ ] , dtype = scale . dtype ) , scale = array_ops . ones ( [ ] , dtype = scale . dtype ) ) , bijector = AffineLinearOperator ( shift = loc , scale = scale , validate_args = validate_args ) , batch_shape = batch_shape , event_shape = event_shape , validate_args = validate_args , name = name ) self . _parameters = parameters