I defined the model  which based on logistic regression.
I don not know the predict method by logistic regression.
I thought that it is needed to correct input_data (data queue[1,8]) to the list(tmp) till length of list become 64.
After that the list of input_data substitute the model. like → pred = model(input_data)
Finally, label probabilities are output . [0.34,-0.2] (1×6)
So pred is calculated. pred = torch.argmax(pred, dim=1)
is my idea correct?
The model
import torch
import torch.nn as nn
import numpy as np
class LogisticRegression(nn.Module):
    def __init__(self, input_size, num_classes):
        super(LogisticRegression, self).__init__()
        self.linear = nn.Linear(input_size, num_classes)
        self.linear_1 = nn.Linear(num_classes, 6400)
    def forward(self, x):
       # x = torch.randn(1, 64).float()
        x = torch.zeros(1, 64, dtype=torch.float)
        y = torch.zeros(1, 6, dtype=torch.float)  # 2
        x = self.linear(x)
        output = self.linear_1(x)
        return output