If you wrap all modules in an nn.Sequential module, you are not using the original forward method anymore, and thus are missing this reshape operation 2.
You could add a custom Flatten layer or write your own forward method using a custom nn.Module:
class Flatten(nn.Module):
    def __init__(self):
        super(Flatten, self).__init__()
        
    def forward(self, x):
        x = x.view(x.size(0), -1)
        return x

last_layer = list(resnet152.children())[-1]
modules=list(resnet152.children())[:-1]
resnet152=nn.Sequential(*modules)
for p in resnet152.parameters():
    p.requires_grad = False

resnet152.fc = nn.Sequential(
    Flatten(),
    last_layer
)

x = torch.randn(2, 3, 224, 224)
output = resnet152(x)