I copy-pasted your model from your original post and added used my code snippet from the previous post.
I needed to define self.log_softmax as nn.LogSoftmax(1), as it was missing.
Here is the complete code:
class CharCNN(nn.Module):
    def __init__(self):
        super(CharCNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv1d(num_channels, depth_1, kernel_size=kernel_size_1, stride=stride_size),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=kernel_size_1, stride=stride_size),
            nn.Dropout(0.1),
        )
        
        self.conv2 = nn.Sequential(
            nn.Conv1d(depth_1, depth_2, kernel_size=kernel_size_2, stride=stride_size),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=3, stride=3),
            nn.Dropout(0.25)
        )            
            
        self.fc1 = nn.Sequential(
            nn.Linear(depth_2*kernel_size_2, num_hidden),
            nn.ReLU(),
            nn.Dropout(0.5)
        )
        
        self.fc2 = nn.Sequential(
            nn.Linear(num_hidden, 11),
            nn.ReLU(),
            nn.Dropout(0.5)
        )
        self.log_softmax = nn.LogSoftmax(1)
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
    
        # collapse
        x = x.view(x.size(0), -1)
        # linear layer
        x = self.fc1(x)
        # output layer
        x = self.fc2(x)
        x = self.log_softmax(x)
        
        return x

num_channels = 52
depth_1 = 128
kernel_size_1 = 7
stride_size = 3
depth_2 = 128
kernel_size_2 = 3
num_hidden = 512
model = CharCNN()

x = torch.randn(64, 52, 300)
out = model(x)
print(out.shape)
> torch.Size([64, 11])

Most likely you’ve changed something in the code, so I would recommend to reuse your original code, as it keeps the batch size.