code: def __init__(
      self, in_channels, out_channels, kernel_size=1, bias=True
  ):
      super(ConvReLULN1D, self).__init__()
      self.act = nn.ReLU(inplace=True)
      self.net = nn.Sequential(
          nn.Conv1d(
              in_channels=in_channels,
              out_channels=out_channels,
              kernel_size=kernel_size,
              bias=bias,
          ),
          self.act,
      )
      self.norm = nn.LayerNorm(out_channels)
  
imports: from abc import ABC, abstractmethod
  import torch
  import torch.nn
  import torch.nn.functional
  import math
  from .multimodal_encoder.builder import build_vision_tower
  import pdb
  from ferret.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
label_desc: Use image node_id 1 to create a new ConvReLULN1D object from inherited base classes: nn with example: obj = ConvReLULN1D(in_channels, out_channels, kernel_size, bias)