úÎߧ#      !"None"#-<hµ Session is all the  Parameters and a #< type Session a = (M.HashMap String (Parameter a), Context)A parameter is two $ to back a % Possible exception in  ºFor every symbol in the neural network, it can be placeholder or a variable. therefore, a Config is to specify the shape of the placeholder and the method to initialize the variables.Note that it is not right to specify a symbol as both placeholder and initializer, although it is tolerated and such a symbol is considered as a variable.[Note that any symbol not specified will be initialized with the _cfg_default_initializer.AInitializer is about how to create a NDArray from a given shape. 9Usually, it can be a wrapper of MXNet operators, such as random_uniform,  random_normal,  random_gamma, etc.. TrainM is a StateT monad Execute the  monad@infer the shapes of all the symbols in a symbolic neural networkinitialize all parameters&0bind the symbolic network with actual parameters5single step train. Must provide all the placeholders.Eforward only. Must provide all the placeholders, setting the data to Just xx, and set label to Nothing.ONote that the batch size here can be different from that in the training phase.''modify the state within the inner monadWthanks to lens, we can modify the first field of the state with following combinator:UmodifyT . traverseOf _1 :: (Field1 s s a b, Monad m) => (a -> m b) -> StateT s m ()      Safe"#None-<STh‰ !" !"(       !"#$%&$'($)*+,-'mxnet-nn-0.0.1.2-8QJIN4MH2RgIncb5JnI5stMXNet.NNMXNet.NN.UtilsMXNet.NN.LayerSession _sess_param _sess_context Parameter _param_in _param_grad$fShowParameterExcMismatchedShapeConfig_cfg_placeholders_cfg_initializers_cfg_default_initializer _cfg_context Optimizer InitializerTrainM sess_context sess_paramtrain inferShape initializefit forwardOnly getContext$fExceptionExc $fShowExc formatShape formatContextvariable convolutionfullyConnected$mxnet-0.2.0.0-1udUBGya0TT3R5TlVSrea9MXNet.Core.Base.DTypeContextMXNet.Core.Base.NDArrayNDArrayMXNet.Core.Base.SymbolSymbolbindmodifyT