Hey,
I think something like this could work for you:
self.conv1.weight = torch.nn.Parameter(initial_param['weight'])
self.conv1.bias = torch.nn.Parameter(initial_param['bias'])

For example, the following code runs without error:
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F

class Kernel_Emb(nn.Module):

    def __init__(self,D_in,H,D_out):
        super(Kernel_Emb, self).__init__()
        self.linear1 = nn.Linear(D_in,H)
        self.linear2 = nn.Linear(H,D_out)
        self.linear1.weight = torch.nn.Parameter(torch.zeros(D_in,H))
        self.linear1.bias = torch.nn.Parameter(torch.ones(H))
    
    
    def forward(self, x):
        h_relu = self.linear1(x).clamp(min=0)
        y_pred = self.linear2(h_relu)
        return y_pred

net = Kernel_Emb(embd_dim,H1,kernel_dim)
print(net.linear1.bias)  ###this prints ones.