class_docstr: Conv1d --> GroupNorm --> Mish
  
  Parameters:
      inp_channels (`int`): Number of input channels.
      out_channels (`int`): Number of output channels.
      kernel_size (`int` or `tuple`): Size of the convolving kernel.
      n_groups (`int`, default `8`): Number of groups to separate the channels into.
class_docstr_tok: ["Conv1d","--",">","GroupNorm","--",">","Mish","Parameters",":","inp_channels","(","`","int","`",")",":","Number","of","input","channels",".","out_channels","(","`","int","`",")",":","Number","of","output","channels",".","kernel_size","(","`","int","`","or","`","tuple","`",")",":","Size","of","the","convolving","kernel",".","n_groups","(","`","int","`",",","default","`","8","`",")",":","Number","of","groups","to","separate","the","channels","into","."]
code: def forward(self, inputs: torch.Tensor) -> torch.Tensor:
      intermediate_repr = self.conv1d(inputs)
      intermediate_repr = rearrange_dims(intermediate_repr)
      intermediate_repr = self.group_norm(intermediate_repr)
      intermediate_repr = rearrange_dims(intermediate_repr)
      output = self.mish(intermediate_repr)
      return output
  
imports: from functools import partial
  from typing import Optional, Tuple, Union
  import torch
  import torch.nn
  import torch.nn.functional
  from ..utils import USE_PEFT_BACKEND
  from .activations import get_activation
  from .attention import AdaGroupNorm
  from .attention_processor import SpatialNorm
  from .lora import LoRACompatibleConv, LoRACompatibleLinear
label_desc: Use image node_id 2 for calling the Conv1dBlock obj's underlying member method code with example usage: obj.forward(inputs) and returns: output