͑iSSKJr SSKJr SSKrSSKJr \(aSSKJr /r Sq Sq S Sjr S Sjr S S S jjrg) ) annotations) TYPE_CHECKINGN) check_type) Initializerc[$zH Return the global weight initializer, The user doesn't need to use it. )_global_weight_initializer_W/var/www/html/banglarbhumi/venv/lib/python3.13/site-packages/paddle/base/initializer.py_global_weight_initializerr s  '&r c[$r )_global_bias_initializer_r r r _global_bias_initializerr's  %$r c[US[RRR[ S54S5 Uq[US[RRR[ S54S5 Uqg)aG This API is used to set up global model parameter initializer in framework. After this API is invoked, the global initializer will takes effect in subsequent code. The model parameters include ``weight`` and ``bias`` . In the framework, they correspond to ``paddle.ParamAttr`` , which is inherited from ``paddle.Tensor`` , and is a persistable Variable. This API only takes effect for model parameters, not for variables created through apis such as :ref:`api_paddle_static_create_global_var` , :ref:`api_paddle_Tensor_create_tensor`. If the initializer is also set up by ``param_attr`` or ``bias_attr`` when creating a network layer, the global initializer setting here will not take effect because it has a lower priority. If you want to cancel the global initializer in framework, please set global initializer to ``None`` . Args: weight_init (Initializer|None): set the global initializer for ``weight`` of model parameters. bias_init (Initializer|None, optional): set the global initializer for ``bias`` of model parameters. Default: None. Returns: None Examples: .. code-block:: python >>> import paddle >>> import paddle.nn as nn >>> nn.initializer.set_global_initializer(nn.initializer.Uniform(), nn.initializer.Constant()) >>> x_var = paddle.uniform((2, 4, 8, 8), dtype='float32', min=-1., max=1.) >>> # The weight of conv1 is initialized by Uniform >>> # The bias of conv1 is initialized by Constant >>> conv1 = nn.Conv2D(4, 6, (3, 3)) >>> y_var1 = conv1(x_var) >>> # If set param_attr/bias_attr too, global initializer will not take effect >>> # The weight of conv2 is initialized by Xavier >>> # The bias of conv2 is initialized by Normal >>> conv2 = nn.Conv2D(4, 6, (3, 3), ... weight_attr=nn.initializer.XavierUniform(), ... bias_attr=nn.initializer.Normal()) >>> y_var2 = conv2(x_var) >>> # Cancel the global initializer in framework, it will takes effect in subsequent code >>> nn.initializer.set_global_initializer(None) weight_initNset_global_initializer bias_init)rpaddlenn initializerrtyper r)rrs r rr.soh    * *DJ7 #.    * *DJ7 !*r )returnInitializer | None)N)rrrrrNone) __future__rtypingrr data_feederrpaddle.nn.initializerr__all__r rrrrr r r r"s\# #1 " '%FJD*#D*0BD* D*r