code: def __init__(
      self,
      in_channels,
      depth_multiplier=1,
      kernel_size=3,
      stride=1,
      padding=0,
      dilation=1,
      bias=True,
      padding_mode="zeros",
  ):
      out_channels = in_channels * depth_multiplier
      super().__init__(
          in_channels=in_channels,
          out_channels=out_channels,
          kernel_size=kernel_size,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=in_channels,
          bias=bias,
          padding_mode=padding_mode,
      )
  
imports: import math
  from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_efficientnet import EfficientNetConfig
label_desc: Use image node_id 1 to create a new EfficientNetDepthwiseConv2d object from inherited base classes: nn with example: obj = EfficientNetDepthwiseConv2d(in_channels, depth_multiplier, kernel_size, stride, padding, dilation, bias, padding_mode)