Feeding model with list of inputs

Would you please guide me regarding this error:

 output =  model(b)

  File "..", line 489, in __call__
    result = self.forward(*input, **kwargs)

  File "...", line 27, in forward
    output = F.relu(self.fc1(input))

  File "...", line 489, in __call__
    result = self.forward(*input, **kwargs)

  File "...", line 67, in forward
    return F.linear(input, self.weight, self.bias)

  File "...", line 1350, in linear
    if input.dim() == 2 and bias is not None:

AttributeError: 'list' object has no attribute 'dim'

For this code:

import torch
import torch.nn as nn
import torch.nn.functional as F
class Test(nn.Module):
        def __init__(self, input_size=2, hidden_size=100):
            self.fc1 = nn.Linear(input_size, hidden_size)
            self.fc2 = nn.Linear(hidden_size, hidden_size)
            self.fc3 = nn.Linear(hidden_size, 1)
            nn.init.constant_(self.fc1.bias, 0)
            nn.init.constant_(self.fc2.bias, 0)
            nn.init.constant_(self.fc3.bias, 0)
        def forward(self, input):
            output = F.relu(self.fc1(input))
            output = F.relu(self.fc2(output))
            output = self.fc3(output)
            return output

model = Test()
input1 = torch.randn(1, 3,16, 16)
input2 = torch.randn(1, 3,16, 16)

b = [input1, input2]

output =  model(b)


You can pass whatever as input but you can feed built-in layers with tensors.
to do what you are doing you need something like
Analogous ways:
output = list(map(model,b))
output = [model(f) for f in b]

output = []
for f in b:

Thank you very much for your reply, @JuanFMontesinos.
It worked.