neulab/docprompting-conala huggingface dataset:
class torch.nn.MarginRankingLoss(margin=0.0, size_average=None, reduce=None, reduction='mean') [source]
 
Creates a criterion that measures the loss given inputs x1x1 , x2x2 , two 1D mini-batch Tensors, and a label 1D mini-batch tensor yy  (containing 1 or -1). If y=1y = 1  then it assumed the first input should be ranked higher (have a larger value) than the second input, and vice-versa for y=−1y = -1 . The loss function for each pair of samples in the mini-batch is:  loss(x1,x2,y)=max⁡(0,−y∗(x1−x2)+margin)\text{loss}(x1, x2, y) = \max(0, -y * (x1 - x2) + \text{margin})  
 Parameters 
 
margin (float, optional) – Has a default value of 00 . 
size_average (bool, optional) – Deprecated (see reduction). By default, the losses are averaged over each loss element in the batch. Note that for some losses, there are multiple elements per sample. If the field size_average is set to False, the losses are instead summed for each minibatch. Ignored when reduce is False. Default: True
 
reduce (bool, optional) – Deprecated (see reduction). By default, the losses are averaged or summed over observations for each minibatch depending on size_average. When reduce is False, returns a loss per batch element instead and ignores size_average. Default: True
 
reduction (string, optional) – Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean': the sum of the output will be divided by the number of elements in the output, 'sum': the output will be summed. Note: size_average and reduce are in the process of being deprecated, and in the meantime, specifying either of those two args will override reduction. Default: 'mean'
     Shape:

 Input1: (N)(N)  where N is the batch size. Input2: (N)(N) , same shape as the Input1. Target: (N)(N) , same shape as the inputs. Output: scalar. If reduction is 'none', then (N)(N) .    Examples: >>> loss = nn.MarginRankingLoss()
>>> input1 = torch.randn(3, requires_grad=True)
>>> input2 = torch.randn(3, requires_grad=True)
>>> target = torch.randn(3).sign()
>>> output = loss(input1, input2, target)
>>> output.backward()
class torch.nn.MaxPool1d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) [source]
 
Applies a 1D max pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size (N,C,L)(N, C, L)  and output (N,C,Lout)(N, C, L_{out})  can be precisely described as:  out(Ni,Cj,k)=max⁡m=0,…,kernel_size−1input(Ni,Cj,stride×k+m)out(N_i, C_j, k) = \max_{m=0, \ldots, \text{kernel\_size} - 1} input(N_i, C_j, stride \times k + m)  
If padding is non-zero, then the input is implicitly padded with negative infinity on both sides for padding number of points. dilation is the stride between the elements within the sliding window. This link has a nice visualization of the pooling parameters.  Note When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.   Parameters 
 
kernel_size – The size of the sliding window, must be > 0. 
stride – The stride of the sliding window, must be > 0. Default value is kernel_size. 
padding – Implicit negative infinity padding to be added on both sides, must be >= 0 and <= kernel_size / 2. 
dilation – The stride between elements within a sliding window, must be > 0. 
return_indices – If True, will return the argmax along with the max values. Useful for torch.nn.MaxUnpool1d later 
ceil_mode – If True, will use ceil instead of floor to compute the output shape. This ensures that every element in the input tensor is covered by a sliding window.     Shape:

 Input: (N,C,Lin)(N, C, L_{in}) 
 
Output: (N,C,Lout)(N, C, L_{out}) , where  Lout=⌊Lin+2×padding−dilation×(kernel_size−1)−1stride+1⌋L_{out} = \left\lfloor \frac{L_{in} + 2 \times \text{padding} - \text{dilation} \times (\text{kernel\_size} - 1) - 1}{\text{stride}} + 1\right\rfloor  
    Examples: >>> # pool of size=3, stride=2
>>> m = nn.MaxPool1d(3, stride=2)
>>> input = torch.randn(20, 16, 50)
>>> output = m(input)
class torch.nn.MaxPool2d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) [source]
 
Applies a 2D max pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size (N,C,H,W)(N, C, H, W) , output (N,C,Hout,Wout)(N, C, H_{out}, W_{out})  and kernel_size (kH,kW)(kH, kW)  can be precisely described as:  out(Ni,Cj,h,w)=max⁡m=0,…,kH−1max⁡n=0,…,kW−1input(Ni,Cj,stride[0]×h+m,stride[1]×w+n)\begin{aligned} out(N_i, C_j, h, w) ={} & \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\ & \text{input}(N_i, C_j, \text{stride[0]} \times h + m, \text{stride[1]} \times w + n) \end{aligned}  
If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. dilation controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what dilation does.  Note When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.  The parameters kernel_size, stride, padding, dilation can either be:  a single int – in which case the same value is used for the height and width dimension a tuple of two ints – in which case, the first int is used for the height dimension, and the second int for the width dimension   Parameters 
 
kernel_size – the size of the window to take a max over 
stride – the stride of the window. Default value is kernel_size
 
padding – implicit zero padding to be added on both sides 
dilation – a parameter that controls the stride of elements in the window 
return_indices – if True, will return the max indices along with the outputs. Useful for torch.nn.MaxUnpool2d later 
ceil_mode – when True, will use ceil instead of floor to compute the output shape     Shape:

 Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in}) 
 
Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) , where  Hout=⌊Hin+2∗padding[0]−dilation[0]×(kernel_size[0]−1)−1stride[0]+1⌋H_{out} = \left\lfloor\frac{H_{in} + 2 * \text{padding[0]} - \text{dilation[0]} \times (\text{kernel\_size[0]} - 1) - 1}{\text{stride[0]}} + 1\right\rfloor  
 Wout=⌊Win+2∗padding[1]−dilation[1]×(kernel_size[1]−1)−1stride[1]+1⌋W_{out} = \left\lfloor\frac{W_{in} + 2 * \text{padding[1]} - \text{dilation[1]} \times (\text{kernel\_size[1]} - 1) - 1}{\text{stride[1]}} + 1\right\rfloor  
    Examples: >>> # pool of square window of size=3, stride=2
>>> m = nn.MaxPool2d(3, stride=2)
>>> # pool of non-square window
>>> m = nn.MaxPool2d((3, 2), stride=(2, 1))
>>> input = torch.randn(20, 16, 50, 32)
>>> output = m(input)
class torch.nn.MaxPool3d(kernel_size, stride=None, padding=0, dilation=1, return_indices=False, ceil_mode=False) [source]
 
Applies a 3D max pooling over an input signal composed of several input planes. In the simplest case, the output value of the layer with input size (N,C,D,H,W)(N, C, D, H, W) , output (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out})  and kernel_size (kD,kH,kW)(kD, kH, kW)  can be precisely described as:  out(Ni,Cj,d,h,w)=max⁡k=0,…,kD−1max⁡m=0,…,kH−1max⁡n=0,…,kW−1input(Ni,Cj,stride[0]×d+k,stride[1]×h+m,stride[2]×w+n)\begin{aligned} \text{out}(N_i, C_j, d, h, w) ={} & \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\ & \text{input}(N_i, C_j, \text{stride[0]} \times d + k, \text{stride[1]} \times h + m, \text{stride[2]} \times w + n) \end{aligned}  
If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points. dilation controls the spacing between the kernel points. It is harder to describe, but this link has a nice visualization of what dilation does.  Note When ceil_mode=True, sliding windows are allowed to go off-bounds if they start within the left padding or the input. Sliding windows that would start in the right padded region are ignored.  The parameters kernel_size, stride, padding, dilation can either be:  a single int – in which case the same value is used for the depth, height and width dimension a tuple of three ints – in which case, the first int is used for the depth dimension, the second int for the height dimension and the third int for the width dimension   Parameters 
 
kernel_size – the size of the window to take a max over 
stride – the stride of the window. Default value is kernel_size
 
padding – implicit zero padding to be added on all three sides 
dilation – a parameter that controls the stride of elements in the window 
return_indices – if True, will return the max indices along with the outputs. Useful for torch.nn.MaxUnpool3d later 
ceil_mode – when True, will use ceil instead of floor to compute the output shape     Shape:

 Input: (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in}) 
 
Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) , where  Dout=⌊Din+2×padding[0]−dilation[0]×(kernel_size[0]−1)−1stride[0]+1⌋D_{out} = \left\lfloor\frac{D_{in} + 2 \times \text{padding}[0] - \text{dilation}[0] \times (\text{kernel\_size}[0] - 1) - 1}{\text{stride}[0]} + 1\right\rfloor  
 Hout=⌊Hin+2×padding[1]−dilation[1]×(kernel_size[1]−1)−1stride[1]+1⌋H_{out} = \left\lfloor\frac{H_{in} + 2 \times \text{padding}[1] - \text{dilation}[1] \times (\text{kernel\_size}[1] - 1) - 1}{\text{stride}[1]} + 1\right\rfloor  
 Wout=⌊Win+2×padding[2]−dilation[2]×(kernel_size[2]−1)−1stride[2]+1⌋W_{out} = \left\lfloor\frac{W_{in} + 2 \times \text{padding}[2] - \text{dilation}[2] \times (\text{kernel\_size}[2] - 1) - 1}{\text{stride}[2]} + 1\right\rfloor  
    Examples: >>> # pool of square window of size=3, stride=2
>>> m = nn.MaxPool3d(3, stride=2)
>>> # pool of non-square window
>>> m = nn.MaxPool3d((3, 2, 2), stride=(2, 1, 2))
>>> input = torch.randn(20, 16, 50,44, 31)
>>> output = m(input)
class torch.nn.MaxUnpool1d(kernel_size, stride=None, padding=0) [source]
 
Computes a partial inverse of MaxPool1d. MaxPool1d is not fully invertible, since the non-maximal values are lost. MaxUnpool1d takes in as input the output of MaxPool1d including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero.  Note MaxPool1d can map several input sizes to the same output sizes. Hence, the inversion process can get ambiguous. To accommodate this, you can provide the needed output size as an additional argument output_size in the forward call. See the Inputs and Example below.   Parameters 
 
kernel_size (int or tuple) – Size of the max pooling window. 
stride (int or tuple) – Stride of the max pooling window. It is set to kernel_size by default. 
padding (int or tuple) – Padding that was added to the input     Inputs:

 
input: the input Tensor to invert 
indices: the indices given out by MaxPool1d
 
output_size (optional): the targeted output size   Shape:

 Input: (N,C,Hin)(N, C, H_{in}) 
 
Output: (N,C,Hout)(N, C, H_{out}) , where  Hout=(Hin−1)×stride[0]−2×padding[0]+kernel_size[0]H_{out} = (H_{in} - 1) \times \text{stride}[0] - 2 \times \text{padding}[0] + \text{kernel\_size}[0]  
or as given by output_size in the call operator     Example: >>> pool = nn.MaxPool1d(2, stride=2, return_indices=True)
>>> unpool = nn.MaxUnpool1d(2, stride=2)
>>> input = torch.tensor([[[1., 2, 3, 4, 5, 6, 7, 8]]])
>>> output, indices = pool(input)
>>> unpool(output, indices)
tensor([[[ 0.,  2.,  0.,  4.,  0.,  6.,  0., 8.]]])

>>> # Example showcasing the use of output_size
>>> input = torch.tensor([[[1., 2, 3, 4, 5, 6, 7, 8, 9]]])
>>> output, indices = pool(input)
>>> unpool(output, indices, output_size=input.size())
tensor([[[ 0.,  2.,  0.,  4.,  0.,  6.,  0., 8.,  0.]]])

>>> unpool(output, indices)
tensor([[[ 0.,  2.,  0.,  4.,  0.,  6.,  0., 8.]]])
class torch.nn.MaxUnpool2d(kernel_size, stride=None, padding=0) [source]
 
Computes a partial inverse of MaxPool2d. MaxPool2d is not fully invertible, since the non-maximal values are lost. MaxUnpool2d takes in as input the output of MaxPool2d including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero.  Note MaxPool2d can map several input sizes to the same output sizes. Hence, the inversion process can get ambiguous. To accommodate this, you can provide the needed output size as an additional argument output_size in the forward call. See the Inputs and Example below.   Parameters 
 
kernel_size (int or tuple) – Size of the max pooling window. 
stride (int or tuple) – Stride of the max pooling window. It is set to kernel_size by default. 
padding (int or tuple) – Padding that was added to the input     Inputs:

 
input: the input Tensor to invert 
indices: the indices given out by MaxPool2d
 
output_size (optional): the targeted output size   Shape:

 Input: (N,C,Hin,Win)(N, C, H_{in}, W_{in}) 
 
Output: (N,C,Hout,Wout)(N, C, H_{out}, W_{out}) , where  Hout=(Hin−1)×stride[0]−2×padding[0]+kernel_size[0]H_{out} = (H_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}  
 Wout=(Win−1)×stride[1]−2×padding[1]+kernel_size[1]W_{out} = (W_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}  
or as given by output_size in the call operator     Example: >>> pool = nn.MaxPool2d(2, stride=2, return_indices=True)
>>> unpool = nn.MaxUnpool2d(2, stride=2)
>>> input = torch.tensor([[[[ 1.,  2,  3,  4],
                            [ 5,  6,  7,  8],
                            [ 9, 10, 11, 12],
                            [13, 14, 15, 16]]]])
>>> output, indices = pool(input)
>>> unpool(output, indices)
tensor([[[[  0.,   0.,   0.,   0.],
          [  0.,   6.,   0.,   8.],
          [  0.,   0.,   0.,   0.],
          [  0.,  14.,   0.,  16.]]]])

>>> # specify a different output size than input size
>>> unpool(output, indices, output_size=torch.Size([1, 1, 5, 5]))
tensor([[[[  0.,   0.,   0.,   0.,   0.],
          [  6.,   0.,   8.,   0.,   0.],
          [  0.,   0.,   0.,  14.,   0.],
          [ 16.,   0.,   0.,   0.,   0.],
          [  0.,   0.,   0.,   0.,   0.]]]])
class torch.nn.MaxUnpool3d(kernel_size, stride=None, padding=0) [source]
 
Computes a partial inverse of MaxPool3d. MaxPool3d is not fully invertible, since the non-maximal values are lost. MaxUnpool3d takes in as input the output of MaxPool3d including the indices of the maximal values and computes a partial inverse in which all non-maximal values are set to zero.  Note MaxPool3d can map several input sizes to the same output sizes. Hence, the inversion process can get ambiguous. To accommodate this, you can provide the needed output size as an additional argument output_size in the forward call. See the Inputs section below.   Parameters 
 
kernel_size (int or tuple) – Size of the max pooling window. 
stride (int or tuple) – Stride of the max pooling window. It is set to kernel_size by default. 
padding (int or tuple) – Padding that was added to the input     Inputs:

 
input: the input Tensor to invert 
indices: the indices given out by MaxPool3d
 
output_size (optional): the targeted output size   Shape:

 Input: (N,C,Din,Hin,Win)(N, C, D_{in}, H_{in}, W_{in}) 
 
Output: (N,C,Dout,Hout,Wout)(N, C, D_{out}, H_{out}, W_{out}) , where  Dout=(Din−1)×stride[0]−2×padding[0]+kernel_size[0]D_{out} = (D_{in} - 1) \times \text{stride[0]} - 2 \times \text{padding[0]} + \text{kernel\_size[0]}  
 Hout=(Hin−1)×stride[1]−2×padding[1]+kernel_size[1]H_{out} = (H_{in} - 1) \times \text{stride[1]} - 2 \times \text{padding[1]} + \text{kernel\_size[1]}  
 Wout=(Win−1)×stride[2]−2×padding[2]+kernel_size[2]W_{out} = (W_{in} - 1) \times \text{stride[2]} - 2 \times \text{padding[2]} + \text{kernel\_size[2]}  
or as given by output_size in the call operator     Example: >>> # pool of square window of size=3, stride=2
>>> pool = nn.MaxPool3d(3, stride=2, return_indices=True)
>>> unpool = nn.MaxUnpool3d(3, stride=2)
>>> output, indices = pool(torch.randn(20, 16, 51, 33, 15))
>>> unpooled_output = unpool(output, indices)
>>> unpooled_output.size()
torch.Size([20, 16, 51, 33, 15])
add_module(name, module) [source]
 
Adds a child module to the current module. The module can be accessed as an attribute using the given name.  Parameters 
 
name (string) – name of the child module. The child module can be accessed from this module using the given name 
module (Module) – child module to be added to the module.
apply(fn) [source]
 
Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model (see also torch.nn.init).  Parameters 
fn (Module -> None) – function to be applied to each submodule  Returns 
self  Return type 
Module   Example: >>> @torch.no_grad()
>>> def init_weights(m):
>>>     print(m)
>>>     if type(m) == nn.Linear:
>>>         m.weight.fill_(1.0)
>>>         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Linear(in_features=2, out_features=2, bias=True)
Parameter containing:
tensor([[ 1.,  1.],
        [ 1.,  1.]])
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
bfloat16() [source]
 
Casts all floating point parameters and buffers to bfloat16 datatype.  Returns 
self  Return type 
Module
buffers(recurse=True) [source]
 
Returns an iterator over module buffers.  Parameters 
recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.  Yields 
torch.Tensor – module buffer   Example: >>> for buf in model.buffers():
>>>     print(type(buf), buf.size())
<class 'torch.Tensor'> (20L,)
<class 'torch.Tensor'> (20L, 1L, 5L, 5L)
children() [source]
 
Returns an iterator over immediate children modules.  Yields 
Module – a child module
cpu() [source]
 
Moves all model parameters and buffers to the CPU.  Returns 
self  Return type 
Module
cuda(device=None) [source]
 
Moves all model parameters and buffers to the GPU. This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.  Parameters 
device (int, optional) – if specified, all parameters will be copied to that device  Returns 
self  Return type 
Module
double() [source]
 
Casts all floating point parameters and buffers to double datatype.  Returns 
self  Return type 
Module
dump_patches: bool = False  
This allows better BC support for load_state_dict(). In state_dict(), the version number will be saved as in the attribute _metadata of the returned state dict, and thus pickled. _metadata is a dictionary with keys that follow the naming convention of state dict. See _load_from_state_dict on how to use this information in loading. If new parameters/buffers are added/removed from a module, this number shall be bumped, and the module’s _load_from_state_dict method can compare the version number and do appropriate changes if the state dict is from before the change.
eval() [source]
 
Sets the module in evaluation mode. This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm, etc. This is equivalent with self.train(False).  Returns 
self  Return type 
Module
extra_repr() [source]
 
Set the extra representation of the module To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.
float() [source]
 
Casts all floating point parameters and buffers to float datatype.  Returns 
self  Return type 
Module
forward(*input)  
Defines the computation performed at every call. Should be overridden by all subclasses.  Note Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.
half() [source]
 
Casts all floating point parameters and buffers to half datatype.  Returns 
self  Return type 
Module
load_state_dict(state_dict, strict=True) [source]
 
Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.  Parameters 
 
state_dict (dict) – a dict containing parameters and persistent buffers. 
strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True
   Returns 
 
missing_keys is a list of str containing the missing keys 
unexpected_keys is a list of str containing the unexpected keys   Return type 
NamedTuple with missing_keys and unexpected_keys fields
modules() [source]
 
Returns an iterator over all modules in the network.  Yields 
Module – a module in the network    Note Duplicate modules are returned only once. In the following example, l will be returned only once.  Example: >>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
        print(idx, '->', m)

0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)
named_buffers(prefix='', recurse=True) [source]
 
Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.  Parameters 
 
prefix (str) – prefix to prepend to all buffer names. 
recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.   Yields 
(string, torch.Tensor) – Tuple containing the name and buffer   Example: >>> for name, buf in self.named_buffers():
>>>    if name in ['running_var']:
>>>        print(buf.size())
named_children() [source]
 
Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.  Yields 
(string, Module) – Tuple containing a name and child module   Example: >>> for name, module in model.named_children():
>>>     if name in ['conv4', 'conv5']:
>>>         print(module)
named_modules(memo=None, prefix='') [source]
 
Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.  Yields 
(string, Module) – Tuple of name and module    Note Duplicate modules are returned only once. In the following example, l will be returned only once.  Example: >>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
        print(idx, '->', m)

0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))
named_parameters(prefix='', recurse=True) [source]
 
Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.  Parameters 
 
prefix (str) – prefix to prepend to all parameter names. 
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.   Yields 
(string, Parameter) – Tuple containing the name and parameter   Example: >>> for name, param in self.named_parameters():
>>>    if name in ['bias']:
>>>        print(param.size())