parameters: (self,
                 alpha,
                 beta,
                 validate_args=True,
                 allow_nan_stats=False,
                 name="InverseGamma")
docstring: Construct InverseGamma distributions with parameters `alpha` and `beta`.
  
      The parameters `alpha` and `beta` must be shaped in a way that supports
      broadcasting (e.g. `alpha + beta` is a valid operation).
  
      Args:
        alpha: Floating point tensor, the shape params of the
          distribution(s).
          alpha must contain only positive values.
        beta: Floating point tensor, the scale params of the distribution(s).
          beta must contain only positive values.
        validate_args: Whether to assert that `a > 0, b > 0`, and that `x > 0` in
          the methods `prob(x)` and `log_prob(x)`.  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 prepend to all ops created by this distribution.
  
      Raises:
        TypeError: if `alpha` and `beta` are different dtypes.
docstring_summary: Construct InverseGamma distributions with parameters `alpha` and `beta`.
docstring_tokens: Construct InverseGamma distributions with parameters alpha and beta .
function: def __init__(self,
                 alpha,
                 beta,
                 validate_args=True,
                 allow_nan_stats=False,
                 name="InverseGamma"):
      """Construct InverseGamma distributions with parameters `alpha` and `beta`.
  
      The parameters `alpha` and `beta` must be shaped in a way that supports
      broadcasting (e.g. `alpha + beta` is a valid operation).
  
      Args:
        alpha: Floating point tensor, the shape params of the
          distribution(s).
          alpha must contain only positive values.
        beta: Floating point tensor, the scale params of the distribution(s).
          beta must contain only positive values.
        validate_args: Whether to assert that `a > 0, b > 0`, and that `x > 0` in
          the methods `prob(x)` and `log_prob(x)`.  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 prepend to all ops created by this distribution.
  
      Raises:
        TypeError: if `alpha` and `beta` are different dtypes.
      """
      self._allow_nan_stats = allow_nan_stats
      self._validate_args = validate_args
      with ops.op_scope([alpha, beta], name) as scope:
        self._name = scope
        with ops.control_dependencies([check_ops.assert_positive(
            alpha), check_ops.assert_positive(beta)] if validate_args else []):
          alpha = array_ops.identity(alpha, name="alpha")
          beta = array_ops.identity(beta, name="beta")
  
          contrib_tensor_util.assert_same_float_dtype((alpha, beta))
          self._broadcast_tensor = alpha + beta
  
      self._get_batch_shape = self._broadcast_tensor.get_shape()
      self._get_event_shape = tensor_shape.TensorShape([])
  
      self._alpha = alpha
      self._beta = beta
function_tokens: def __init__ ( self , alpha , beta , validate_args = True , allow_nan_stats = False , name = "InverseGamma" ) : self . _allow_nan_stats = allow_nan_stats self . _validate_args = validate_args with ops . op_scope ( [ alpha , beta ] , name ) as scope : self . _name = scope with ops . control_dependencies ( [ check_ops . assert_positive ( alpha ) , check_ops . assert_positive ( beta ) ] if validate_args else [ ] ) : alpha = array_ops . identity ( alpha , name = "alpha" ) beta = array_ops . identity ( beta , name = "beta" ) contrib_tensor_util . assert_same_float_dtype ( ( alpha , beta ) ) self . _broadcast_tensor = alpha + beta self . _get_batch_shape = self . _broadcast_tensor . get_shape ( ) self . _get_event_shape = tensor_shape . TensorShape ( [ ] ) self . _alpha = alpha self . _beta = beta