tensorflow-haskell/docs/haddock/tensorflow-0.2.0.0/tensorflow.txt

-- Hoogle documentation, generated by Haddock
-- See Hoogle, http://www.haskell.org/hoogle/


-- | TensorFlow bindings.
--   
--   This library provides an interface to the TensorFlow bindings.
--   <a>TensorFlow.Core</a> contains the base API for building and running
--   computational graphs. Other packages such as <tt>tensorflow-ops</tt>
--   contain bindings to the actual computational kernels.
--   
--   For more documentation and examples, see
--   <a>https://github.com/tensorflow/haskell#readme</a>
@package tensorflow
@version 0.2.0.0

module TensorFlow.Internal.FFI
data TensorFlowException
TensorFlowException :: Code -> Text -> TensorFlowException
data Session

-- | Runs the given action after creating a session with options populated
--   by the given optionSetter.
withSession :: (MonadIO m, MonadMask m) => (SessionOptions -> IO ()) -> ((IO () -> IO ()) -> Session -> m a) -> m a
extendGraph :: Session -> GraphDef -> IO ()
run :: Session -> [(ByteString, TensorData)] -> [ByteString] -> [ByteString] -> IO [TensorData]

-- | All of the data needed to represent a tensor.
data TensorData
TensorData :: [Int64] -> !DataType -> !(Vector Word8) -> TensorData
[tensorDataDimensions] :: TensorData -> [Int64]
[tensorDataType] :: TensorData -> !DataType
[tensorDataBytes] :: TensorData -> !(Vector Word8)
setSessionConfig :: ConfigProto -> SessionOptions -> IO ()
setSessionTarget :: ByteString -> SessionOptions -> IO ()

-- | Returns the serialized OpList of all OpDefs defined in this address
--   space.
getAllOpList :: IO ByteString

-- | Serializes the given msg and provides it as (ptr,len) argument to the
--   given action.
useProtoAsVoidPtrLen :: (Message msg, Integral c, Show c, Bits c) => msg -> (Ptr b -> c -> IO a) -> IO a
instance GHC.Classes.Eq TensorFlow.Internal.FFI.TensorData
instance GHC.Show.Show TensorFlow.Internal.FFI.TensorData
instance GHC.Classes.Eq TensorFlow.Internal.FFI.TensorFlowException
instance GHC.Show.Show TensorFlow.Internal.FFI.TensorFlowException
instance GHC.Exception.Exception TensorFlow.Internal.FFI.TensorFlowException


-- | Originally taken from internal proto-lens code.
module TensorFlow.Internal.VarInt

-- | Decode an unsigned varint.
getVarInt :: Parser Word64

-- | Encode a Word64.
putVarInt :: Word64 -> Builder

module TensorFlow.Types

-- | The class of scalar types supported by tensorflow.
class TensorType a
tensorType :: TensorType a => a -> DataType
tensorRefType :: TensorType a => a -> DataType
tensorVal :: TensorType a => Lens' TensorProto [a]

-- | Tensor data with the correct memory layout for tensorflow.
newtype TensorData a
TensorData :: TensorData -> TensorData a
[unTensorData] :: TensorData a -> TensorData

-- | Types that can be converted to and from <a>TensorData</a>.
--   
--   <a>Vector</a> is the most efficient to encode/decode for most element
--   types.
class TensorType a => TensorDataType s a

-- | Decode the bytes of a <a>TensorData</a> into an <a>s</a>.
decodeTensorData :: TensorDataType s a => TensorData a -> s a

-- | Encode an <a>s</a> into a <a>TensorData</a>.
--   
--   The values should be in row major order, e.g.,
--   
--   element 0: index (0, ..., 0) element 1: index (0, ..., 1) ...
encodeTensorData :: TensorDataType s a => Shape -> s a -> TensorData a
newtype Scalar a
Scalar :: a -> Scalar a
[unScalar] :: Scalar a -> a

-- | Shape (dimensions) of a tensor.
--   
--   TensorFlow supports shapes of unknown rank, which are represented as
--   <tt>Nothing :: Maybe Shape</tt> in Haskell.
newtype Shape
Shape :: [Int64] -> Shape
protoShape :: Lens' TensorShapeProto Shape
class Attribute a
attrLens :: Attribute a => Lens' AttrValue a
data DataType :: *
DT_INVALID :: DataType
DT_FLOAT :: DataType
DT_DOUBLE :: DataType
DT_INT32 :: DataType
DT_UINT8 :: DataType
DT_INT16 :: DataType
DT_INT8 :: DataType
DT_STRING :: DataType
DT_COMPLEX64 :: DataType
DT_INT64 :: DataType
DT_BOOL :: DataType
DT_QINT8 :: DataType
DT_QUINT8 :: DataType
DT_QINT32 :: DataType
DT_BFLOAT16 :: DataType
DT_QINT16 :: DataType
DT_QUINT16 :: DataType
DT_UINT16 :: DataType
DT_COMPLEX128 :: DataType
DT_HALF :: DataType
DT_RESOURCE :: DataType
DT_VARIANT :: DataType
DT_UINT32 :: DataType
DT_UINT64 :: DataType
DT_FLOAT_REF :: DataType
DT_DOUBLE_REF :: DataType
DT_INT32_REF :: DataType
DT_UINT8_REF :: DataType
DT_INT16_REF :: DataType
DT_INT8_REF :: DataType
DT_STRING_REF :: DataType
DT_COMPLEX64_REF :: DataType
DT_INT64_REF :: DataType
DT_BOOL_REF :: DataType
DT_QINT8_REF :: DataType
DT_QUINT8_REF :: DataType
DT_QINT32_REF :: DataType
DT_BFLOAT16_REF :: DataType
DT_QINT16_REF :: DataType
DT_QUINT16_REF :: DataType
DT_UINT16_REF :: DataType
DT_COMPLEX128_REF :: DataType
DT_HALF_REF :: DataType
DT_RESOURCE_REF :: DataType
DT_VARIANT_REF :: DataType
DT_UINT32_REF :: DataType
DT_UINT64_REF :: DataType
type ResourceHandle = ResourceHandleProto

-- | Dynamic type. TensorFlow variants aren't supported yet. This type acts
--   a placeholder to simplify op generation.
data Variant

-- | A heterogeneous list type.
data ListOf f as
[Nil] :: ListOf f '[]
[:/] :: f a -> ListOf f as -> ListOf f (a : as)
type List = ListOf Identity

-- | Equivalent of <a>:/</a> for lists.
(/:/) :: a -> List as -> List (a : as)
infixr 5 /:/
data TensorTypeProxy a
[TensorTypeProxy] :: TensorType a => TensorTypeProxy a
class TensorTypes (ts :: [*])
tensorTypes :: TensorTypes ts => TensorTypeList ts
type TensorTypeList = ListOf TensorTypeProxy
fromTensorTypeList :: TensorTypeList ts -> [DataType]
fromTensorTypes :: forall as. TensorTypes as => Proxy as -> [DataType]

-- | A <a>Constraint</a> specifying the possible choices of a
--   <a>TensorType</a>.
--   
--   We implement a <a>Constraint</a> like <tt>OneOf '[Double, Float]
--   a</tt> by turning the natural representation as a conjunction, i.e.,
--   
--   <pre>
--   a == Double || a == Float
--   </pre>
--   
--   into a disjunction like
--   
--   <pre>
--   a /= Int32 &amp;&amp; a /= Int64 &amp;&amp; a /= ByteString &amp;&amp; ...
--   </pre>
--   
--   using an enumeration of all the possible <a>TensorType</a>s.
type OneOf ts a = (TensorType a, TensorTypes' ts, NoneOf (AllTensorTypes \\ ts) a)

-- | A constraint checking that two types are different.
type OneOfs ts as = (TensorTypes as, TensorTypes' ts, NoneOfs (AllTensorTypes \\ ts) as)

-- | Helper types to produce a reasonable type error message when the
--   Constraint "a /= a" fails. TODO(judahjacobson): Use ghc-8's
--   CustomTypeErrors for this.
data TypeError a
data ExcludedCase

-- | A constraint that the type <tt>a</tt> doesn't appear in the type list
--   <tt>ts</tt>. Assumes that <tt>a</tt> and each of the elements of
--   <tt>ts</tt> are <a>TensorType</a>s.

-- | Takes the difference of two lists of types.

-- | Removes a type from the given list of types.

-- | An enumeration of all valid <a>TensorType</a>s.
type AllTensorTypes = '[Float, Double, Int8, Int16, Int32, Int64, Word8, Word16, ByteString, Bool]
instance GHC.Show.Show TensorFlow.Types.Shape
instance Data.String.IsString a => Data.String.IsString (TensorFlow.Types.Scalar a)
instance GHC.Real.RealFrac a => GHC.Real.RealFrac (TensorFlow.Types.Scalar a)
instance GHC.Float.RealFloat a => GHC.Float.RealFloat (TensorFlow.Types.Scalar a)
instance GHC.Real.Real a => GHC.Real.Real (TensorFlow.Types.Scalar a)
instance GHC.Float.Floating a => GHC.Float.Floating (TensorFlow.Types.Scalar a)
instance GHC.Real.Fractional a => GHC.Real.Fractional (TensorFlow.Types.Scalar a)
instance GHC.Num.Num a => GHC.Num.Num (TensorFlow.Types.Scalar a)
instance GHC.Classes.Ord a => GHC.Classes.Ord (TensorFlow.Types.Scalar a)
instance GHC.Classes.Eq a => GHC.Classes.Eq (TensorFlow.Types.Scalar a)
instance GHC.Show.Show a => GHC.Show.Show (TensorFlow.Types.Scalar a)
instance TensorFlow.Types.TensorTypes '[]
instance (TensorFlow.Types.TensorType t, TensorFlow.Types.TensorTypes ts) => TensorFlow.Types.TensorTypes (t : ts)
instance TensorFlow.Types.All GHC.Classes.Eq (TensorFlow.Types.Map f as) => GHC.Classes.Eq (TensorFlow.Types.ListOf f as)
instance TensorFlow.Types.All GHC.Show.Show (TensorFlow.Types.Map f as) => GHC.Show.Show (TensorFlow.Types.ListOf f as)
instance TensorFlow.Types.Attribute GHC.Types.Float
instance TensorFlow.Types.Attribute Data.ByteString.Internal.ByteString
instance TensorFlow.Types.Attribute GHC.Int.Int64
instance TensorFlow.Types.Attribute Proto.Tensorflow.Core.Framework.Types.DataType
instance TensorFlow.Types.Attribute Proto.Tensorflow.Core.Framework.Tensor.TensorProto
instance TensorFlow.Types.Attribute GHC.Types.Bool
instance TensorFlow.Types.Attribute TensorFlow.Types.Shape
instance TensorFlow.Types.Attribute (GHC.Base.Maybe TensorFlow.Types.Shape)
instance TensorFlow.Types.Attribute Proto.Tensorflow.Core.Framework.AttrValue.AttrValue'ListValue
instance TensorFlow.Types.Attribute [Proto.Tensorflow.Core.Framework.Types.DataType]
instance TensorFlow.Types.Attribute [GHC.Int.Int64]
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Types.Float
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Types.Double
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Int.Int8
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Int.Int16
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Int.Int32
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Int.Int64
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Word.Word8
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Word.Word16
instance TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector GHC.Types.Bool
instance (Foreign.Storable.Storable a, TensorFlow.Types.TensorDataType Data.Vector.Storable.Vector a, TensorFlow.Types.TensorType a) => TensorFlow.Types.TensorDataType Data.Vector.Vector a
instance TensorFlow.Types.TensorDataType Data.Vector.Vector (Data.Complex.Complex GHC.Types.Float)
instance TensorFlow.Types.TensorDataType Data.Vector.Vector (Data.Complex.Complex GHC.Types.Double)
instance TensorFlow.Types.TensorDataType Data.Vector.Vector Data.ByteString.Internal.ByteString
instance (TensorFlow.Types.TensorDataType Data.Vector.Vector a, TensorFlow.Types.TensorType a) => TensorFlow.Types.TensorDataType TensorFlow.Types.Scalar a
instance GHC.Exts.IsList TensorFlow.Types.Shape
instance TensorFlow.Types.TensorType GHC.Types.Float
instance TensorFlow.Types.TensorType GHC.Types.Double
instance TensorFlow.Types.TensorType GHC.Int.Int32
instance TensorFlow.Types.TensorType GHC.Int.Int64
instance TensorFlow.Types.TensorType GHC.Word.Word8
instance TensorFlow.Types.TensorType GHC.Word.Word16
instance TensorFlow.Types.TensorType GHC.Word.Word32
instance TensorFlow.Types.TensorType GHC.Word.Word64
instance TensorFlow.Types.TensorType GHC.Int.Int16
instance TensorFlow.Types.TensorType GHC.Int.Int8
instance TensorFlow.Types.TensorType Data.ByteString.Internal.ByteString
instance TensorFlow.Types.TensorType GHC.Types.Bool
instance TensorFlow.Types.TensorType (Data.Complex.Complex GHC.Types.Float)
instance TensorFlow.Types.TensorType (Data.Complex.Complex GHC.Types.Double)
instance TensorFlow.Types.TensorType TensorFlow.Types.ResourceHandle
instance TensorFlow.Types.TensorType TensorFlow.Types.Variant

module TensorFlow.Output

-- | A type of graph node which has no outputs. These nodes are valuable
--   for causing side effects when they are run.
newtype ControlNode
ControlNode :: NodeName -> ControlNode
[unControlNode] :: ControlNode -> NodeName

-- | A device that a node can be assigned to. There's a naming convention
--   where the device names are constructed from job and replica names.
newtype Device
Device :: Text -> Device
[deviceName] :: Device -> Text

-- | The name of a node in the graph. This corresponds to the proto field
--   NodeDef.name. Includes the scope prefix (if any) and a unique
--   identifier (if the node was implicitly named).
newtype NodeName
NodeName :: Text -> NodeName
[unNodeName] :: NodeName -> Text

-- | Op definition. This corresponds somewhat to the <tt>NodeDef</tt>
--   proto.
data OpDef
OpDef :: !PendingNodeName -> !OpType -> !(Map Text AttrValue) -> [Output] -> [NodeName] -> OpDef
[_opName] :: OpDef -> !PendingNodeName
[_opType] :: OpDef -> !OpType
[_opAttrs] :: OpDef -> !(Map Text AttrValue)
[_opInputs] :: OpDef -> [Output]
[_opControlInputs] :: OpDef -> [NodeName]
opName :: Lens' OpDef PendingNodeName
opType :: Lens' OpDef OpType
opAttr :: Attribute a => Text -> Lens' OpDef a
opInputs :: Lens' OpDef [Output]
opControlInputs :: Lens' OpDef [NodeName]

-- | The type of op of a node in the graph. This corresponds to the proto
--   field NodeDef.op.
newtype OpType
OpType :: Text -> OpType
[unOpType] :: OpType -> Text
newtype OutputIx
OutputIx :: Int -> OutputIx
[unOutputIx] :: OutputIx -> Int

-- | An output of a TensorFlow node.
data Output
Output :: !OutputIx -> !NodeName -> Output
[outputIndex] :: Output -> !OutputIx
[outputNodeName] :: Output -> !NodeName
output :: OutputIx -> NodeName -> Output

-- | The name specified for an unrendered Op. If an Op has an ImplicitName,
--   it will be assigned based on the opType plus a unique identifier. Does
--   not contain the "scope" prefix.
data PendingNodeName
ExplicitName :: !Text -> PendingNodeName
ImplicitName :: PendingNodeName
instance GHC.Classes.Ord TensorFlow.Output.OpDef
instance GHC.Classes.Eq TensorFlow.Output.OpDef
instance GHC.Show.Show TensorFlow.Output.Output
instance GHC.Classes.Ord TensorFlow.Output.Output
instance GHC.Classes.Eq TensorFlow.Output.Output
instance GHC.Show.Show TensorFlow.Output.NodeName
instance GHC.Classes.Ord TensorFlow.Output.NodeName
instance GHC.Classes.Eq TensorFlow.Output.NodeName
instance GHC.Show.Show TensorFlow.Output.PendingNodeName
instance GHC.Classes.Ord TensorFlow.Output.PendingNodeName
instance GHC.Classes.Eq TensorFlow.Output.PendingNodeName
instance Data.String.IsString TensorFlow.Output.Device
instance GHC.Classes.Ord TensorFlow.Output.Device
instance GHC.Classes.Eq TensorFlow.Output.Device
instance GHC.Show.Show TensorFlow.Output.OutputIx
instance GHC.Enum.Enum TensorFlow.Output.OutputIx
instance GHC.Num.Num TensorFlow.Output.OutputIx
instance GHC.Classes.Ord TensorFlow.Output.OutputIx
instance GHC.Classes.Eq TensorFlow.Output.OutputIx
instance GHC.Show.Show TensorFlow.Output.OpType
instance GHC.Classes.Ord TensorFlow.Output.OpType
instance GHC.Classes.Eq TensorFlow.Output.OpType
instance Data.String.IsString TensorFlow.Output.Output
instance Data.String.IsString TensorFlow.Output.PendingNodeName
instance GHC.Show.Show TensorFlow.Output.Device
instance Data.String.IsString TensorFlow.Output.OpType

module TensorFlow.Build

-- | A type of graph node which has no outputs. These nodes are valuable
--   for causing side effects when they are run.
newtype ControlNode
ControlNode :: NodeName -> ControlNode
[unControlNode] :: ControlNode -> NodeName
data Unique
explicitName :: Text -> PendingNodeName
implicitName :: PendingNodeName
opDef :: OpType -> OpDef
opDefWithName :: PendingNodeName -> OpType -> OpDef
opName :: Lens' OpDef PendingNodeName
opType :: Lens' OpDef OpType
opAttr :: Attribute a => Text -> Lens' OpDef a
opInputs :: Lens' OpDef [Output]
opControlInputs :: Lens' OpDef [NodeName]
data GraphState
renderedNodeDefs :: Lens' GraphState (Map NodeName NodeDef)

-- | An action for building nodes in a TensorFlow graph. Used to manage
--   build state internally as part of the <tt>Session</tt> monad.
data BuildT m a

-- | An action for building nodes in a TensorFlow graph.
type Build = BuildT Identity

-- | Lift a <a>Build</a> action into a monad, including any explicit op
--   renderings.
class Monad m => MonadBuild m
build :: MonadBuild m => Build a -> m a

-- | Registers the given node to be executed before the next <a>run</a>.
addInitializer :: MonadBuild m => ControlNode -> m ()

-- | This is Control.Monad.Morph.hoist sans the dependency.
hoistBuildT :: (forall a. m a -> n a) -> BuildT m b -> BuildT n b
evalBuildT :: Monad m => BuildT m a -> m a
runBuildT :: BuildT m a -> m (a, GraphState)

-- | Produce a GraphDef proto representation of the nodes that are rendered
--   in the given <a>Build</a> action.
asGraphDef :: Build a -> GraphDef
addGraphDef :: MonadBuild m => GraphDef -> m ()

-- | Get all the initializers that have accumulated so far, and clear that
--   buffer.
flushInitializers :: Monad m => BuildT m [NodeName]

-- | Get all the NodeDefs that have accumulated so far, and clear that
--   buffer.
flushNodeBuffer :: MonadBuild m => m [NodeDef]
summaries :: Lens' GraphState [Output]

-- | Render the given op if it hasn't been rendered already, and return its
--   name.
getOrAddOp :: OpDef -> Build NodeName

-- | Add a new node for a given <a>OpDef</a>. This is used for making
--   "stateful" ops which are not safe to dedup (e.g, "variable" and
--   "assign").
addNewOp :: OpDef -> Build NodeName

-- | Turn an <a>Output</a> into a string representation for the TensorFlow
--   foreign APIs.
encodeOutput :: Output -> Text
lookupNode :: NodeName -> Build NodeDef

-- | Modify some part of the state, run an action, and restore the state
--   after that action is done.
withStateLens :: MonadBuild m => Lens' GraphState a -> (a -> a) -> m b -> m b

-- | Set a device for all nodes rendered in the given <a>Build</a> action
--   (unless further overridden by another use of withDevice).
withDevice :: MonadBuild m => Maybe Device -> m a -> m a

-- | Prepend a scope to all nodes rendered in the given <a>Build</a>
--   action.
withNameScope :: MonadBuild m => Text -> m a -> m a

-- | Add control inputs to all nodes rendered in the given <a>Build</a>
--   action.
withNodeDependencies :: MonadBuild m => Set NodeName -> m a -> m a
instance Control.Monad.Fix.MonadFix m => Control.Monad.Fix.MonadFix (TensorFlow.Build.BuildT m)
instance Control.Monad.Catch.MonadMask m => Control.Monad.Catch.MonadMask (TensorFlow.Build.BuildT m)
instance Control.Monad.Catch.MonadCatch m => Control.Monad.Catch.MonadCatch (TensorFlow.Build.BuildT m)
instance Control.Monad.Catch.MonadThrow m => Control.Monad.Catch.MonadThrow (TensorFlow.Build.BuildT m)
instance GHC.Base.Monad m => Control.Monad.State.Class.MonadState TensorFlow.Build.GraphState (TensorFlow.Build.BuildT m)
instance Control.Monad.Trans.Class.MonadTrans TensorFlow.Build.BuildT
instance Control.Monad.IO.Class.MonadIO m => Control.Monad.IO.Class.MonadIO (TensorFlow.Build.BuildT m)
instance GHC.Base.Monad m => GHC.Base.Monad (TensorFlow.Build.BuildT m)
instance GHC.Base.Monad m => GHC.Base.Applicative (TensorFlow.Build.BuildT m)
instance GHC.Base.Functor m => GHC.Base.Functor (TensorFlow.Build.BuildT m)
instance GHC.Classes.Ord TensorFlow.Build.PendingNode
instance GHC.Classes.Eq TensorFlow.Build.PendingNode
instance Data.String.IsString TensorFlow.Build.Scope
instance GHC.Classes.Ord TensorFlow.Build.Scope
instance GHC.Classes.Eq TensorFlow.Build.Scope
instance GHC.Enum.Enum TensorFlow.Build.Unique
instance GHC.Classes.Ord TensorFlow.Build.Unique
instance GHC.Classes.Eq TensorFlow.Build.Unique
instance GHC.Base.Monad m => TensorFlow.Build.MonadBuild (TensorFlow.Build.BuildT m)
instance GHC.Show.Show TensorFlow.Build.Scope

module TensorFlow.Tensor

-- | A named output of a TensorFlow operation.
--   
--   The type parameter <tt>a</tt> is the type of the elements in the
--   <a>Tensor</a>. The parameter <tt>v</tt> is either:
--   
--   <ul>
--   <li><a>Build</a>: An unrendered, immutable value.</li>
--   <li><a>Value</a>: A rendered, immutable value.</li>
--   <li><a>Ref</a>: A rendered stateful handle (e.g., a variable).</li>
--   </ul>
--   
--   Note that <a>expr</a>, <a>value</a>, <a>render</a> and
--   <a>renderValue</a> can help convert between the different types of
--   <a>Tensor</a>.
data Tensor v a
[Tensor] :: TensorKind v => {tensorOutput :: v Output} -> Tensor v a
newtype Value a
Value :: a -> Value a
[runValue] :: Value a -> a
newtype Ref a
Ref :: a -> Ref a
[runRef] :: Ref a -> a

-- | Cast a 'Tensor Ref' into a 'Tensor Value'. This behaves like a no-op.
value :: Tensor Ref a -> Tensor Value a
renderValue :: MonadBuild m => Tensor v a -> m (Tensor Value a)

-- | A pair of a <a>Tensor</a> and some data that should be fed into that
--   <a>Tensor</a> when running the graph.
data Feed
Feed :: Output -> TensorData -> Feed

-- | A class ensuring that a given tensor is rendered, i.e., has a fixed
--   name, device, etc.
class Rendered t
renderedOutput :: Rendered t => t a -> Output
tensorNodeName :: Rendered t => t a -> NodeName

-- | Create a <a>Feed</a> for feeding the given data into a <a>Tensor</a>
--   when running the graph.
--   
--   Note that if a <a>Tensor</a> is rendered, its identity may change; so
--   feeding the rendered <a>Tensor</a> may be different than feeding the
--   original <a>Tensor</a>.
feed :: Rendered t => t a -> TensorData a -> Feed

-- | Create a <a>Tensor</a> for a given name. This can be used to reference
--   nodes in a <tt>GraphDef</tt> that was loaded via <a>addGraphDef</a>.
--   TODO(judahjacobson): add more safety checks here.
tensorFromName :: TensorKind v => Text -> Tensor v a

-- | Like <a>tensorFromName</a>, but type-restricted to <a>Value</a>.
tensorValueFromName :: Text -> Tensor Value a

-- | Like <a>tensorFromName</a>, but type-restricted to <a>Ref</a>.
tensorRefFromName :: Text -> Tensor Ref a
type TensorList v = ListOf (Tensor v)
tensorListOutputs :: Rendered (Tensor v) => TensorList v as -> [Output]

-- | Places all nodes rendered in the given <a>Build</a> action on the same
--   device as the given Tensor (see also <a>withDevice</a>). Make sure
--   that the action has side effects of rendering the desired tensors. A
--   pure return would not have the desired effect.
colocateWith :: (MonadBuild m, Rendered t) => t b -> m a -> m a

-- | Render a <a>Tensor</a>, fixing its name, scope, device and control
--   inputs from the <a>MonadBuild</a> context. Also renders any
--   dependencies of the <a>Tensor</a> that weren't already rendered.
--   
--   This operation is idempotent; calling <a>render</a> on the same input
--   in the same context will produce the same result. However, rendering
--   the same <tt>Tensor Build</tt> in two different contexts may result in
--   two different <tt>Tensor Value</tt>s.
render :: MonadBuild m => Tensor Build a -> m (Tensor Value a)
expr :: TensorKind v => Tensor v a -> Tensor Build a

-- | Records the given summary action in Build for retrieval with Summary
--   protocol buffer in string form. For safety, use the pre-composed
--   functions: Logging.scalarSummary and Logging.histogramSummary.
addSummary :: (MonadBuild m, TensorKind v) => Tensor v ByteString -> m ()

-- | Retrieves the summary ops collected thus far. Typically this only
--   happens once, but if <a>buildWithSummary</a> is used repeatedly, the
--   values accumulate.
collectAllSummaries :: MonadBuild m => m [SummaryTensor]

-- | Synonym for the tensors that return serialized Summary proto.
type SummaryTensor = Tensor Value ByteString

-- | An internal class for kinds of Tensors.
class Monad v => TensorKind v
toBuild :: TensorKind v => v a -> Build a

-- | Types which can be converted to <a>Tensor</a>.
class ToTensor t
toTensor :: (ToTensor t, TensorType a) => t a -> Tensor Build a
instance GHC.Base.Functor TensorFlow.Tensor.Ref
instance GHC.Base.Functor TensorFlow.Tensor.Value
instance TensorFlow.Tensor.TensorKind v => TensorFlow.Tensor.ToTensor (TensorFlow.Tensor.Tensor v)
instance TensorFlow.Tensor.Rendered (TensorFlow.Tensor.Tensor TensorFlow.Tensor.Value)
instance TensorFlow.Tensor.Rendered (TensorFlow.Tensor.Tensor TensorFlow.Tensor.Ref)
instance TensorFlow.Tensor.TensorKind TensorFlow.Tensor.Value
instance TensorFlow.Tensor.TensorKind TensorFlow.Tensor.Ref
instance TensorFlow.Tensor.TensorKind TensorFlow.Build.Build
instance GHC.Base.Applicative TensorFlow.Tensor.Ref
instance GHC.Base.Monad TensorFlow.Tensor.Ref
instance GHC.Base.Applicative TensorFlow.Tensor.Value
instance GHC.Base.Monad TensorFlow.Tensor.Value

module TensorFlow.Nodes

-- | Types that contain ops which can be run.
class Nodes t
getNodes :: Nodes t => t -> Build (Set NodeName)

-- | Types that tensor representations (e.g. <a>Tensor</a>,
--   <a>ControlNode</a>) can be fetched into.
--   
--   Includes collections of tensors (e.g. tuples).
class Nodes t => Fetchable t a
getFetch :: Fetchable t a => t -> Build (Fetch a)

-- | Fetch action. Keeps track of what needs to be fetched and how to
--   decode the fetched data.
data Fetch a
Fetch :: Set Text -> (Map Text TensorData -> a) -> Fetch a

-- | Nodes to fetch
[fetches] :: Fetch a -> Set Text

-- | Function to create an <tt>a</tt> from the fetched data.
[fetchRestore] :: Fetch a -> Map Text TensorData -> a
nodesUnion :: (Monoid b, Traversable t, Applicative f) => t (f b) -> f b
fetchTensorVector :: forall a v. (TensorType a) => Tensor v a -> Build (Fetch (TensorData a))
instance (TensorFlow.Nodes.Fetchable t1 a1, TensorFlow.Nodes.Fetchable t2 a2) => TensorFlow.Nodes.Fetchable (t1, t2) (a1, a2)
instance (TensorFlow.Nodes.Fetchable t1 a1, TensorFlow.Nodes.Fetchable t2 a2, TensorFlow.Nodes.Fetchable t3 a3) => TensorFlow.Nodes.Fetchable (t1, t2, t3) (a1, a2, a3)
instance TensorFlow.Nodes.Fetchable t a => TensorFlow.Nodes.Fetchable [t] [a]
instance TensorFlow.Nodes.Fetchable t a => TensorFlow.Nodes.Fetchable (GHC.Base.Maybe t) (GHC.Base.Maybe a)
instance a ~ () => TensorFlow.Nodes.Fetchable TensorFlow.Output.ControlNode a
instance l ~ TensorFlow.Types.List '[] => TensorFlow.Nodes.Fetchable (TensorFlow.Types.ListOf f '[]) l
instance (TensorFlow.Nodes.Fetchable (f t) a, TensorFlow.Nodes.Fetchable (TensorFlow.Types.ListOf f ts) (TensorFlow.Types.List as), i ~ Data.Functor.Identity.Identity) => TensorFlow.Nodes.Fetchable (TensorFlow.Types.ListOf f (t : ts)) (TensorFlow.Types.ListOf i (a : as))
instance (TensorFlow.Types.TensorType a, a ~ a') => TensorFlow.Nodes.Fetchable (TensorFlow.Tensor.Tensor v a) (TensorFlow.Types.TensorData a')
instance (TensorFlow.Types.TensorType a, TensorFlow.Types.TensorDataType s a, a ~ a') => TensorFlow.Nodes.Fetchable (TensorFlow.Tensor.Tensor v a) (s a')
instance GHC.Base.Functor TensorFlow.Nodes.Fetch
instance GHC.Base.Applicative TensorFlow.Nodes.Fetch
instance (TensorFlow.Nodes.Nodes t1, TensorFlow.Nodes.Nodes t2) => TensorFlow.Nodes.Nodes (t1, t2)
instance (TensorFlow.Nodes.Nodes t1, TensorFlow.Nodes.Nodes t2, TensorFlow.Nodes.Nodes t3) => TensorFlow.Nodes.Nodes (t1, t2, t3)
instance TensorFlow.Nodes.Nodes t => TensorFlow.Nodes.Nodes [t]
instance TensorFlow.Nodes.Nodes t => TensorFlow.Nodes.Nodes (GHC.Base.Maybe t)
instance TensorFlow.Nodes.Nodes TensorFlow.Output.ControlNode
instance TensorFlow.Nodes.Nodes (TensorFlow.Types.ListOf f '[])
instance (TensorFlow.Nodes.Nodes (f a), TensorFlow.Nodes.Nodes (TensorFlow.Types.ListOf f as)) => TensorFlow.Nodes.Nodes (TensorFlow.Types.ListOf f (a : as))
instance TensorFlow.Nodes.Nodes (TensorFlow.Tensor.Tensor v a)

module TensorFlow.Session
type Session = SessionT IO
data SessionT m a

-- | Customization for session. Use the lenses to update:
--   <a>sessionTarget</a>, <a>sessionTracer</a>, <a>sessionConfig</a>.
data Options

-- | Uses the specified config for the created session.
sessionConfig :: Lens' Options ConfigProto

-- | Target can be: "local", ip:port, host:port. The set of supported
--   factories depends on the linked in libraries.
sessionTarget :: Lens' Options ByteString

-- | Uses the given logger to monitor session progress.
sessionTracer :: Lens' Options Tracer

-- | Run <a>Session</a> actions in a new TensorFlow session.
runSession :: (MonadMask m, MonadIO m) => SessionT m a -> m a

-- | Run <a>Session</a> actions in a new TensorFlow session created with
--   the given option setter actions (<a>sessionTarget</a>,
--   <a>sessionConfig</a>).
runSessionWithOptions :: (MonadMask m, MonadIO m) => Options -> SessionT m a -> m a

-- | Lift a <a>Build</a> action into a monad, including any explicit op
--   renderings.
class Monad m => MonadBuild m
build :: MonadBuild m => Build a -> m a

-- | Add all pending rendered nodes to the TensorFlow graph and runs any
--   pending initializers.
--   
--   Note that run, runWithFeeds, etc. will all call this function
--   implicitly.
extend :: MonadIO m => SessionT m ()
addGraphDef :: MonadBuild m => GraphDef -> m ()

-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
--   already rendered, and fetch the corresponding values for <tt>a</tt>.
run :: (MonadIO m, Fetchable t a) => t -> SessionT m a

-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
--   already rendered, feed the given input values, and fetch the
--   corresponding result values for <tt>a</tt>.
runWithFeeds :: (MonadIO m, Fetchable t a) => [Feed] -> t -> SessionT m a

-- | Run a subgraph <tt>t</tt>, rendering and extending any dependent nodes
--   that aren't already rendered. This behaves like <a>run</a> except that
--   it doesn't do any fetches.
run_ :: (MonadIO m, Nodes t) => t -> SessionT m ()

-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
--   already rendered, feed the given input values, and fetch the
--   corresponding result values for <tt>a</tt>. This behaves like
--   <a>runWithFeeds</a> except that it doesn't do any fetches.
runWithFeeds_ :: (MonadIO m, Nodes t) => [Feed] -> t -> SessionT m ()

-- | Starts a concurrent thread which evaluates the given Nodes forever
--   until runSession exits or an exception occurs. Graph extension happens
--   synchronously, but the resultant run proceeds as a separate thread.
asyncProdNodes :: (MonadIO m, Nodes t) => t -> SessionT m ()
instance Control.Monad.Catch.MonadMask m => Control.Monad.Catch.MonadMask (TensorFlow.Session.SessionT m)
instance Control.Monad.Catch.MonadCatch m => Control.Monad.Catch.MonadCatch (TensorFlow.Session.SessionT m)
instance Control.Monad.Catch.MonadThrow m => Control.Monad.Catch.MonadThrow (TensorFlow.Session.SessionT m)
instance Control.Monad.IO.Class.MonadIO m => Control.Monad.IO.Class.MonadIO (TensorFlow.Session.SessionT m)
instance GHC.Base.Monad m => GHC.Base.Monad (TensorFlow.Session.SessionT m)
instance GHC.Base.Monad m => GHC.Base.Applicative (TensorFlow.Session.SessionT m)
instance GHC.Base.Functor m => GHC.Base.Functor (TensorFlow.Session.SessionT m)
instance Data.Default.Class.Default TensorFlow.Session.Options
instance Control.Monad.Trans.Class.MonadTrans TensorFlow.Session.SessionT
instance GHC.Base.Monad m => TensorFlow.Build.MonadBuild (TensorFlow.Session.SessionT m)

module TensorFlow.BuildOp

-- | Class of types that can be used as op outputs.
class BuildResult a
buildResult :: BuildResult a => Result a
buildOp :: BuildResult a => [Int64] -> OpDef -> Build a

-- | Class of types that can be used as op outputs.
class PureResult a
pureResult :: PureResult a => ReaderT (Build OpDef) (State ResultState) a
pureOp :: PureResult a => [Int64] -> Build OpDef -> a

-- | Returns true if all the integers in each tuple are identical. Throws
--   an error with a descriptive message if not.
eqLengthGuard :: [(String, [(String, Int)])] -> Bool
class BuildInputs a
buildInputs :: BuildInputs a => a -> Build [Output]

-- | Parameters to build an op (for example, the node name or optional
--   attributes). TODO: be more type safe.
type OpParams = OpDef -> OpDef
instance GHC.Show.Show TensorFlow.BuildOp.ResultState
instance TensorFlow.BuildOp.BuildInputs a => TensorFlow.BuildOp.BuildInputs [a]
instance TensorFlow.BuildOp.BuildInputs (TensorFlow.Tensor.Tensor v a)
instance TensorFlow.BuildOp.BuildInputs (TensorFlow.Types.ListOf (TensorFlow.Tensor.Tensor v) as)
instance TensorFlow.BuildOp.PureResult (TensorFlow.Tensor.Tensor TensorFlow.Build.Build a)
instance (TensorFlow.BuildOp.PureResult a1, TensorFlow.BuildOp.PureResult a2) => TensorFlow.BuildOp.PureResult (a1, a2)
instance (TensorFlow.BuildOp.PureResult a1, TensorFlow.BuildOp.PureResult a2, TensorFlow.BuildOp.PureResult a3) => TensorFlow.BuildOp.PureResult (a1, a2, a3)
instance (TensorFlow.BuildOp.PureResult a1, TensorFlow.BuildOp.PureResult a2, TensorFlow.BuildOp.PureResult a3, TensorFlow.BuildOp.PureResult a4) => TensorFlow.BuildOp.PureResult (a1, a2, a3, a4)
instance (TensorFlow.BuildOp.PureResult a1, TensorFlow.BuildOp.PureResult a2, TensorFlow.BuildOp.PureResult a3, TensorFlow.BuildOp.PureResult a4, TensorFlow.BuildOp.PureResult a5) => TensorFlow.BuildOp.PureResult (a1, a2, a3, a4, a5)
instance (TensorFlow.BuildOp.PureResult a1, TensorFlow.BuildOp.PureResult a2, TensorFlow.BuildOp.PureResult a3, TensorFlow.BuildOp.PureResult a4, TensorFlow.BuildOp.PureResult a5, TensorFlow.BuildOp.PureResult a6) => TensorFlow.BuildOp.PureResult (a1, a2, a3, a4, a5, a6)
instance (TensorFlow.BuildOp.PureResult a1, TensorFlow.BuildOp.PureResult a2, TensorFlow.BuildOp.PureResult a3, TensorFlow.BuildOp.PureResult a4, TensorFlow.BuildOp.PureResult a5, TensorFlow.BuildOp.PureResult a6, TensorFlow.BuildOp.PureResult a7) => TensorFlow.BuildOp.PureResult (a1, a2, a3, a4, a5, a6, a7)
instance (TensorFlow.BuildOp.PureResult a1, TensorFlow.BuildOp.PureResult a2, TensorFlow.BuildOp.PureResult a3, TensorFlow.BuildOp.PureResult a4, TensorFlow.BuildOp.PureResult a5, TensorFlow.BuildOp.PureResult a6, TensorFlow.BuildOp.PureResult a7, TensorFlow.BuildOp.PureResult a8) => TensorFlow.BuildOp.PureResult (a1, a2, a3, a4, a5, a6, a7, a8)
instance TensorFlow.BuildOp.PureResult a => TensorFlow.BuildOp.PureResult [a]
instance TensorFlow.Types.TensorTypes as => TensorFlow.BuildOp.PureResult (TensorFlow.Tensor.TensorList TensorFlow.Build.Build as)
instance (TensorFlow.BuildOp.BuildResult a1, TensorFlow.BuildOp.BuildResult a2) => TensorFlow.BuildOp.BuildResult (a1, a2)
instance (TensorFlow.BuildOp.BuildResult a1, TensorFlow.BuildOp.BuildResult a2, TensorFlow.BuildOp.BuildResult a3) => TensorFlow.BuildOp.BuildResult (a1, a2, a3)
instance (TensorFlow.BuildOp.BuildResult a1, TensorFlow.BuildOp.BuildResult a2, TensorFlow.BuildOp.BuildResult a3, TensorFlow.BuildOp.BuildResult a4) => TensorFlow.BuildOp.BuildResult (a1, a2, a3, a4)
instance (TensorFlow.BuildOp.BuildResult a1, TensorFlow.BuildOp.BuildResult a2, TensorFlow.BuildOp.BuildResult a3, TensorFlow.BuildOp.BuildResult a4, TensorFlow.BuildOp.BuildResult a5) => TensorFlow.BuildOp.BuildResult (a1, a2, a3, a4, a5)
instance (TensorFlow.BuildOp.BuildResult a1, TensorFlow.BuildOp.BuildResult a2, TensorFlow.BuildOp.BuildResult a3, TensorFlow.BuildOp.BuildResult a4, TensorFlow.BuildOp.BuildResult a5, TensorFlow.BuildOp.BuildResult a6) => TensorFlow.BuildOp.BuildResult (a1, a2, a3, a4, a5, a6)
instance (TensorFlow.BuildOp.BuildResult a1, TensorFlow.BuildOp.BuildResult a2, TensorFlow.BuildOp.BuildResult a3, TensorFlow.BuildOp.BuildResult a4, TensorFlow.BuildOp.BuildResult a5, TensorFlow.BuildOp.BuildResult a6, TensorFlow.BuildOp.BuildResult a7) => TensorFlow.BuildOp.BuildResult (a1, a2, a3, a4, a5, a6, a7)
instance (TensorFlow.BuildOp.BuildResult a1, TensorFlow.BuildOp.BuildResult a2, TensorFlow.BuildOp.BuildResult a3, TensorFlow.BuildOp.BuildResult a4, TensorFlow.BuildOp.BuildResult a5, TensorFlow.BuildOp.BuildResult a6, TensorFlow.BuildOp.BuildResult a7, TensorFlow.BuildOp.BuildResult a8) => TensorFlow.BuildOp.BuildResult (a1, a2, a3, a4, a5, a6, a7, a8)
instance (TensorFlow.Tensor.TensorKind v, TensorFlow.Tensor.Rendered (TensorFlow.Tensor.Tensor v)) => TensorFlow.BuildOp.BuildResult (TensorFlow.Tensor.Tensor v a)
instance TensorFlow.BuildOp.BuildResult TensorFlow.Output.ControlNode
instance (TensorFlow.Tensor.TensorKind v, TensorFlow.Tensor.Rendered (TensorFlow.Tensor.Tensor v), TensorFlow.Types.TensorTypes as) => TensorFlow.BuildOp.BuildResult (TensorFlow.Tensor.TensorList v as)
instance TensorFlow.BuildOp.BuildResult a => TensorFlow.BuildOp.BuildResult [a]

module TensorFlow.ControlFlow

-- | Modify a <a>Build</a> action, such that all new ops rendered in it
--   will depend on the nodes in the first argument.
withControlDependencies :: (MonadBuild m, Nodes t) => t -> m a -> m a

-- | Create an op that groups multiple operations.
--   
--   When this op finishes, all ops in the input <tt>n</tt> have finished.
--   This op has no output.
group :: (MonadBuild m, Nodes t) => t -> m ControlNode

-- | Does nothing. Only useful as a placeholder for control edges.
noOp :: MonadBuild m => m ControlNode


-- | The core functionality of TensorFlow.
--   
--   Unless you are defining ops, you do not need to import other modules
--   from this package.
--   
--   Basic ops are provided in the tensorflow-ops and tensorflow-core-ops
--   packages.
module TensorFlow.Core
type Session = SessionT IO

-- | Customization for session. Use the lenses to update:
--   <a>sessionTarget</a>, <a>sessionTracer</a>, <a>sessionConfig</a>.
data Options

-- | Uses the specified config for the created session.
sessionConfig :: Lens' Options ConfigProto

-- | Target can be: "local", ip:port, host:port. The set of supported
--   factories depends on the linked in libraries.
sessionTarget :: Lens' Options ByteString

-- | Uses the given logger to monitor session progress.
sessionTracer :: Lens' Options Tracer

-- | Run <a>Session</a> actions in a new TensorFlow session.
runSession :: (MonadMask m, MonadIO m) => SessionT m a -> m a

-- | Run <a>Session</a> actions in a new TensorFlow session created with
--   the given option setter actions (<a>sessionTarget</a>,
--   <a>sessionConfig</a>).
runSessionWithOptions :: (MonadMask m, MonadIO m) => Options -> SessionT m a -> m a

-- | Lift a <a>Build</a> action into a monad, including any explicit op
--   renderings.
class Monad m => MonadBuild m
build :: MonadBuild m => Build a -> m a

-- | Types that tensor representations (e.g. <a>Tensor</a>,
--   <a>ControlNode</a>) can be fetched into.
--   
--   Includes collections of tensors (e.g. tuples).
class Nodes t => Fetchable t a

-- | Types that contain ops which can be run.
class Nodes t

-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
--   already rendered, and fetch the corresponding values for <tt>a</tt>.
run :: (MonadIO m, Fetchable t a) => t -> SessionT m a

-- | Run a subgraph <tt>t</tt>, rendering and extending any dependent nodes
--   that aren't already rendered. This behaves like <a>run</a> except that
--   it doesn't do any fetches.
run_ :: (MonadIO m, Nodes t) => t -> SessionT m ()

-- | A pair of a <a>Tensor</a> and some data that should be fed into that
--   <a>Tensor</a> when running the graph.
data Feed

-- | Create a <a>Feed</a> for feeding the given data into a <a>Tensor</a>
--   when running the graph.
--   
--   Note that if a <a>Tensor</a> is rendered, its identity may change; so
--   feeding the rendered <a>Tensor</a> may be different than feeding the
--   original <a>Tensor</a>.
feed :: Rendered t => t a -> TensorData a -> Feed

-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
--   already rendered, feed the given input values, and fetch the
--   corresponding result values for <tt>a</tt>.
runWithFeeds :: (MonadIO m, Fetchable t a) => [Feed] -> t -> SessionT m a

-- | Run a subgraph <tt>t</tt>, rendering any dependent nodes that aren't
--   already rendered, feed the given input values, and fetch the
--   corresponding result values for <tt>a</tt>. This behaves like
--   <a>runWithFeeds</a> except that it doesn't do any fetches.
runWithFeeds_ :: (MonadIO m, Nodes t) => [Feed] -> t -> SessionT m ()

-- | Starts a concurrent thread which evaluates the given Nodes forever
--   until runSession exits or an exception occurs. Graph extension happens
--   synchronously, but the resultant run proceeds as a separate thread.
asyncProdNodes :: (MonadIO m, Nodes t) => t -> SessionT m ()

-- | An action for building nodes in a TensorFlow graph.
type Build = BuildT Identity

-- | An action for building nodes in a TensorFlow graph. Used to manage
--   build state internally as part of the <tt>Session</tt> monad.
data BuildT m a

-- | Render a <a>Tensor</a>, fixing its name, scope, device and control
--   inputs from the <a>MonadBuild</a> context. Also renders any
--   dependencies of the <a>Tensor</a> that weren't already rendered.
--   
--   This operation is idempotent; calling <a>render</a> on the same input
--   in the same context will produce the same result. However, rendering
--   the same <tt>Tensor Build</tt> in two different contexts may result in
--   two different <tt>Tensor Value</tt>s.
render :: MonadBuild m => Tensor Build a -> m (Tensor Value a)

-- | Produce a GraphDef proto representation of the nodes that are rendered
--   in the given <a>Build</a> action.
asGraphDef :: Build a -> GraphDef
addGraphDef :: MonadBuild m => GraphDef -> m ()
opName :: Lens' OpDef PendingNodeName
opAttr :: Attribute a => Text -> Lens' OpDef a

-- | Registers the given node to be executed before the next <a>run</a>.
addInitializer :: MonadBuild m => ControlNode -> m ()

-- | A type of graph node which has no outputs. These nodes are valuable
--   for causing side effects when they are run.
data ControlNode

-- | A named output of a TensorFlow operation.
--   
--   The type parameter <tt>a</tt> is the type of the elements in the
--   <a>Tensor</a>. The parameter <tt>v</tt> is either:
--   
--   <ul>
--   <li><a>Build</a>: An unrendered, immutable value.</li>
--   <li><a>Value</a>: A rendered, immutable value.</li>
--   <li><a>Ref</a>: A rendered stateful handle (e.g., a variable).</li>
--   </ul>
--   
--   Note that <a>expr</a>, <a>value</a>, <a>render</a> and
--   <a>renderValue</a> can help convert between the different types of
--   <a>Tensor</a>.
data Tensor v a
data Value a
data Ref a

-- | Cast a 'Tensor Ref' into a 'Tensor Value'. This behaves like a no-op.
value :: Tensor Ref a -> Tensor Value a

-- | Create a <a>Tensor</a> for a given name. This can be used to reference
--   nodes in a <tt>GraphDef</tt> that was loaded via <a>addGraphDef</a>.
--   TODO(judahjacobson): add more safety checks here.
tensorFromName :: TensorKind v => Text -> Tensor v a
expr :: TensorKind v => Tensor v a -> Tensor Build a

-- | The class of scalar types supported by tensorflow.
class TensorType a

-- | Tensor data with the correct memory layout for tensorflow.
data TensorData a

-- | Types that can be converted to and from <a>TensorData</a>.
--   
--   <a>Vector</a> is the most efficient to encode/decode for most element
--   types.
class TensorType a => TensorDataType s a

-- | Decode the bytes of a <a>TensorData</a> into an <a>s</a>.
decodeTensorData :: TensorDataType s a => TensorData a -> s a

-- | Encode an <a>s</a> into a <a>TensorData</a>.
--   
--   The values should be in row major order, e.g.,
--   
--   element 0: index (0, ..., 0) element 1: index (0, ..., 1) ...
encodeTensorData :: TensorDataType s a => Shape -> s a -> TensorData a
type ResourceHandle = ResourceHandleProto
newtype Scalar a
Scalar :: a -> Scalar a
[unScalar] :: Scalar a -> a

-- | Shape (dimensions) of a tensor.
--   
--   TensorFlow supports shapes of unknown rank, which are represented as
--   <tt>Nothing :: Maybe Shape</tt> in Haskell.
newtype Shape
Shape :: [Int64] -> Shape

-- | A <a>Constraint</a> specifying the possible choices of a
--   <a>TensorType</a>.
--   
--   We implement a <a>Constraint</a> like <tt>OneOf '[Double, Float]
--   a</tt> by turning the natural representation as a conjunction, i.e.,
--   
--   <pre>
--   a == Double || a == Float
--   </pre>
--   
--   into a disjunction like
--   
--   <pre>
--   a /= Int32 &amp;&amp; a /= Int64 &amp;&amp; a /= ByteString &amp;&amp; ...
--   </pre>
--   
--   using an enumeration of all the possible <a>TensorType</a>s.
type OneOf ts a = (TensorType a, TensorTypes' ts, NoneOf (AllTensorTypes \\ ts) a)

-- | A constraint checking that two types are different.

-- | Places all nodes rendered in the given <a>Build</a> action on the same
--   device as the given Tensor (see also <a>withDevice</a>). Make sure
--   that the action has side effects of rendering the desired tensors. A
--   pure return would not have the desired effect.
colocateWith :: (MonadBuild m, Rendered t) => t b -> m a -> m a

-- | A device that a node can be assigned to. There's a naming convention
--   where the device names are constructed from job and replica names.
newtype Device
Device :: Text -> Device
[deviceName] :: Device -> Text

-- | Set a device for all nodes rendered in the given <a>Build</a> action
--   (unless further overridden by another use of withDevice).
withDevice :: MonadBuild m => Maybe Device -> m a -> m a

-- | Prepend a scope to all nodes rendered in the given <a>Build</a>
--   action.
withNameScope :: MonadBuild m => Text -> m a -> m a

-- | Modify a <a>Build</a> action, such that all new ops rendered in it
--   will depend on the nodes in the first argument.
withControlDependencies :: (MonadBuild m, Nodes t) => t -> m a -> m a

-- | Create an op that groups multiple operations.
--   
--   When this op finishes, all ops in the input <tt>n</tt> have finished.
--   This op has no output.
group :: (MonadBuild m, Nodes t) => t -> m ControlNode

-- | Does nothing. Only useful as a placeholder for control edges.
noOp :: MonadBuild m => m ControlNode