@Dwight_Foster I was able to add the layers as you mentioned an it works like a charm. But, I have another question what is Bottleneck in the code below:
main_model.backbone.body.layer1.add_module("3", nn.Sequential(nn.Conv2d(1024,256,kernel_size=(1,1),stride=(1,1),bias=False)))

After adding the layer an executing the code below
main_model.backbone.body.layer1

I get this output
Sequential(
  (0): Bottleneck(
    (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn1): FrozenBatchNorm2d(64)
    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): FrozenBatchNorm2d(64)
    (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn3): FrozenBatchNorm2d(256)
    (relu): ReLU(inplace=True)
    (downsample): Sequential(
      (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): FrozenBatchNorm2d(256)
    )
  )
  (1): Bottleneck(
    (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn1): FrozenBatchNorm2d(64)
    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): FrozenBatchNorm2d(64)
    (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn3): FrozenBatchNorm2d(256)
    (relu): ReLU(inplace=True)
  )
  (2): Bottleneck(
    (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn1): FrozenBatchNorm2d(64)
    (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
    (bn2): FrozenBatchNorm2d(64)
    (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
    (bn3): FrozenBatchNorm2d(256)
    (relu): ReLU(inplace=True)
  )
  (3): Sequential(
    (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
  )
)

As you can see it is already a sequential layer and I dont know what is Bottleneck mean ?