code: def forward(self, features: torch.Tensor) -> torch.Tensor:
      if self.config.tf_padding:
          features = apply_tf_padding(features, self.convolution)
      features = self.convolution(features)
      if self.normalization is not None:
          features = self.normalization(features)
      if self.activation is not None:
          features = self.activation(features)
      return features
  
imports: from typing import Optional, Union
  import torch
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import 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_mobilenet_v1 import MobileNetV1Config
label_desc: Use image node_id 2 for calling the MobileNetV1ConvLayer obj's underlying member method code with example usage: obj.forward(features) and returns: features