pymc.SymbolicRandomVariable.__init__#
- SymbolicRandomVariable.__init__(*args, ndim_supp, **kwargs)[source]#
- Parameters
- inputs
The inputs to the graph.
- outputs
The outputs to the graph.
- inline
Defaults to
False
True
: Cause theOp
’s original graph being used during compilation, theOp
will not be visible in the compiled graph but rather its internal graph.False
: will use a pre-compiled function inside.- grad_overrides
Defaults to
'default'
. This argument is mutually exclusive withlop_overrides
.'default'
: Do not override, use default grad() resultOpFromGraph: Override with another OpFromGraph, should accept inputs as the same order and types of
inputs
andoutput_grads
arguments as one would specify in :meth:`Op.grad`() method.callable: Should take two args:
inputs
andoutput_grads
. Each argument is expected to be a list of :class:`Variable `. Must return list of :class:`Variable `.- lop_overrides
Defaults to
'default'
.This argument is mutually exclusive with
grad_overrides
.These options are similar to the
grad_overrides
above, but for theOp.L_op()
method.'default'
: Do not override, use the defaultOp.L_op()
resultOpFromGraph: Override with another OpFromGraph, should accept inputs as the same order and types of
inputs
,outputs
andoutput_grads
arguments as one would specify inOp.grad()
method.callable: Should take three args:
inputs
,outputs
andoutput_grads
. Each argument is expected to be a list ofVariable
. Must return list ofVariable
.NullType instance: Treat as non-differentiable DisconnectedType instance: Treat as disconnected gradient, numerically gives zero
list
: Each OpFromGraph/callable must return a singleVariable
. Each list element corresponds to gradient of a specific input, length of list must be equal to number of inputs.- rop_overrides
One of
{'default', OpFromGraph, callable, Variable}
.Defaults to
'default'
.'default'
: Do not override, use the defaultOp.R_op()
resultOpFromGraph: Override with another OpFromGraph, should accept inputs as the same order and types of
inputs
andeval_points
arguments as one would specify inOp.R_op()
method.callable: Should take two args:
inputs
andeval_points
. Each argument is expected to be a list ofVariable
. Must return list ofVariable
.NullType instance: Treat as non-differentiable DisconnectedType instance: Treat as zero since DisconnectedType is not yet supported in
Op.R_op()
.list
: EachOpFromGraph
/callable must return a singleVariable
. Each list element corresponds to a specific output ofOp.R_op()
, length of list must be equal to number of outputs. connection_pattern If notNone
, this will be used as the connection_pattern for thisOp
.- name
A name for debugging purposes.
- kwargs
Check
pytensor.function()
for more arguments, only works when not inline.