您可以执行以下操作，

from torch import nn
import torch 
dim1 = 3
dim2 = 4 
dim3 = 5 
older_weight_parameters = torch.zeros( dim1, dim2) # replace this with older parameters, older_linear_layer.weight.t().detach()
older_bias_parameters = torch.zeros( dim2) # replace this with older parameters, older_linear_layer.bias.detach()

linear_layer = nn.Linear(dim1, dim2 + dim3)
# This will fill the corresponding parameters with the older parameter values
linear_layer.weight.data[:dim2, :] = older_weight_parameters.t()
linear_layer.bias.data[:dim2] = older_bias_parameters

然后，线性层将具有以下权重和偏置参数值。您可以看到旧参数的值（在示例中这些值为零）已经替换了相应位置的随机参数。

linear_layer.weight, linear_layer.bias
(Parameter containing:
 tensor([[ 0.0000,  0.0000,  0.0000],
         [ 0.0000,  0.0000,  0.0000],
         [ 0.0000,  0.0000,  0.0000],
         [ 0.0000,  0.0000,  0.0000],
         [ 0.3455, -0.3385,  0.4746],
         [-0.2067, -0.4950, -0.3668],
         [-0.4012, -0.3951, -0.3772],
         [-0.1393,  0.3602, -0.0460],
         [-0.4641,  0.2152,  0.4031]], requires_grad=True),
 Parameter containing:
 tensor([ 0.0000,  0.0000,  0.0000,  0.0000,  0.0502, -0.3423,  0.3871, -0.5218,
         -0.4322], requires_grad=True)

线性层的权重和偏移参数与任何其他Tensor类似。你可以对它使用Tensor操作。