parameters: (self,
                 loc=None,
                 scale_diag=None,
                 scale_identity_multiplier=None,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="VectorLaplaceDiag")
docstring: Construct Vector Laplace 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 = 2 * scale @ scale.T`.
  
      ```none
      scale = diag(scale_diag + scale_identity_multiplier * ones(k))
      ```
  
      where:
  
      * `scale_diag.shape = [k]`, and,
      * `scale_identity_multiplier.shape = []`.
  
      Additional leading dimensions (if any) will index batches.
  
      If both `scale_diag` and `scale_identity_multiplier` are `None`, then
      `scale` is the Identity matrix.
  
      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_diag: Non-zero, floating-point `Tensor` representing a diagonal
          matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
          and characterizes `b`-batches of `k x k` diagonal matrices added to
          `scale`. When both `scale_identity_multiplier` and `scale_diag` are
          `None` then `scale` is the `Identity`.
        scale_identity_multiplier: Non-zero, floating-point `Tensor` representing
          a scaled-identity-matrix added to `scale`. May have shape
          `[B1, ..., Bb]`, `b >= 0`, and characterizes `b`-batches of scaled
          `k x k` identity matrices added to `scale`. When both
          `scale_identity_multiplier` and `scale_diag` are `None` then `scale` is
          the `Identity`.
        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.
  
      Raises:
        ValueError: if at most `scale_identity_multiplier` is specified.
docstring_summary: Construct Vector Laplace distribution on `R^k`.
docstring_tokens: Construct Vector Laplace distribution on R^k .
function: def __init__(self,
                 loc=None,
                 scale_diag=None,
                 scale_identity_multiplier=None,
                 validate_args=False,
                 allow_nan_stats=True,
                 name="VectorLaplaceDiag"):
      """Construct Vector Laplace 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 = 2 * scale @ scale.T`.
  
      ```none
      scale = diag(scale_diag + scale_identity_multiplier * ones(k))
      ```
  
      where:
  
      * `scale_diag.shape = [k]`, and,
      * `scale_identity_multiplier.shape = []`.
  
      Additional leading dimensions (if any) will index batches.
  
      If both `scale_diag` and `scale_identity_multiplier` are `None`, then
      `scale` is the Identity matrix.
  
      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_diag: Non-zero, floating-point `Tensor` representing a diagonal
          matrix added to `scale`. May have shape `[B1, ..., Bb, k]`, `b >= 0`,
          and characterizes `b`-batches of `k x k` diagonal matrices added to
          `scale`. When both `scale_identity_multiplier` and `scale_diag` are
          `None` then `scale` is the `Identity`.
        scale_identity_multiplier: Non-zero, floating-point `Tensor` representing
          a scaled-identity-matrix added to `scale`. May have shape
          `[B1, ..., Bb]`, `b >= 0`, and characterizes `b`-batches of scaled
          `k x k` identity matrices added to `scale`. When both
          `scale_identity_multiplier` and `scale_diag` are `None` then `scale` is
          the `Identity`.
        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.
  
      Raises:
        ValueError: if at most `scale_identity_multiplier` is specified.
      """
      parameters = locals()
      with ops.name_scope(name):
        with ops.name_scope("init", values=[
            loc, scale_diag, scale_identity_multiplier]):
          # No need to validate_args while making diag_scale.  The returned
          # LinearOperatorDiag has an assert_non_singular method that is called by
          # the Bijector.
          scale = distribution_util.make_diag_scale(
              loc=loc,
              scale_diag=scale_diag,
              scale_identity_multiplier=scale_identity_multiplier,
              validate_args=False,
              assert_positive=False)
      super(VectorLaplaceDiag, self).__init__(
          loc=loc,
          scale=scale,
          validate_args=validate_args,
          allow_nan_stats=allow_nan_stats,
          name=name)
      self._parameters = parameters
function_tokens: def __init__ ( self , loc = None , scale_diag = None , scale_identity_multiplier = None , validate_args = False , allow_nan_stats = True , name = "VectorLaplaceDiag" ) : parameters = locals ( ) with ops . name_scope ( name ) : with ops . name_scope ( "init" , values = [ loc , scale_diag , scale_identity_multiplier ] ) : # No need to validate_args while making diag_scale.  The returned # LinearOperatorDiag has an assert_non_singular method that is called by # the Bijector. scale = distribution_util . make_diag_scale ( loc = loc , scale_diag = scale_diag , scale_identity_multiplier = scale_identity_multiplier , validate_args = False , assert_positive = False ) super ( VectorLaplaceDiag , self ) . __init__ ( loc = loc , scale = scale , validate_args = validate_args , allow_nan_stats = allow_nan_stats , name = name ) self . _parameters = parameters