parameters: (self,
                 loc,
                 scale,
                 validate_args=True,
                 allow_nan_stats=False,
                 name="Laplace")
docstring: Construct Laplace distribution with parameters `loc` and `scale`.
  
      The parameters `loc` and `scale` must be shaped in a way that supports
      broadcasting (e.g., `loc / scale` is a valid operation).
  
      Args:
        loc: Floating point tensor which characterizes the location (center)
          of the distribution.
        scale: Positive floating point tensor which characterizes the spread of
          the distribution.
        validate_args: Whether to validate input with asserts.  If `validate_args`
          is `False`, and the inputs are invalid, correct behavior is not
          guaranteed.
        allow_nan_stats:  Boolean, default `False`.  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:
        TypeError: if `loc` and `scale` are of different dtype.
docstring_summary: Construct Laplace distribution with parameters `loc` and `scale`.
docstring_tokens: Construct Laplace distribution with parameters loc and scale .
function: def __init__(self,
                 loc,
                 scale,
                 validate_args=True,
                 allow_nan_stats=False,
                 name="Laplace"):
      """Construct Laplace distribution with parameters `loc` and `scale`.
  
      The parameters `loc` and `scale` must be shaped in a way that supports
      broadcasting (e.g., `loc / scale` is a valid operation).
  
      Args:
        loc: Floating point tensor which characterizes the location (center)
          of the distribution.
        scale: Positive floating point tensor which characterizes the spread of
          the distribution.
        validate_args: Whether to validate input with asserts.  If `validate_args`
          is `False`, and the inputs are invalid, correct behavior is not
          guaranteed.
        allow_nan_stats:  Boolean, default `False`.  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:
        TypeError: if `loc` and `scale` are of different dtype.
      """
      self._allow_nan_stats = allow_nan_stats
      self._validate_args = validate_args
      with ops.op_scope([loc, scale], name):
        loc = ops.convert_to_tensor(loc)
        scale = ops.convert_to_tensor(scale)
        with ops.control_dependencies([check_ops.assert_positive(scale)] if
                                      validate_args else []):
          self._name = name
          self._loc = array_ops.identity(loc, name="loc")
          self._scale = array_ops.identity(scale, name="scale")
          self._batch_shape = self._ones().get_shape()
          self._event_shape = tensor_shape.TensorShape([])
  
      contrib_tensor_util.assert_same_float_dtype((loc, scale))
function_tokens: def __init__ ( self , loc , scale , validate_args = True , allow_nan_stats = False , name = "Laplace" ) : self . _allow_nan_stats = allow_nan_stats self . _validate_args = validate_args with ops . op_scope ( [ loc , scale ] , name ) : loc = ops . convert_to_tensor ( loc ) scale = ops . convert_to_tensor ( scale ) with ops . control_dependencies ( [ check_ops . assert_positive ( scale ) ] if validate_args else [ ] ) : self . _name = name self . _loc = array_ops . identity ( loc , name = "loc" ) self . _scale = array_ops . identity ( scale , name = "scale" ) self . _batch_shape = self . _ones ( ) . get_shape ( ) self . _event_shape = tensor_shape . TensorShape ( [ ] ) contrib_tensor_util . assert_same_float_dtype ( ( loc , scale ) )