class_docstr: A module that corresponds to the stem module of the original work.
class_docstr_tok: ["A","module","that","corresponds","to","the","stem","module","of","the","original","work","."]
code: def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
      features = self.padding(pixel_values)
      features = self.convolution(features)
      features = self.batchnorm(features)
      features = self.activation(features)
  
      return features
  
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 2 for calling the EfficientNetEmbeddings obj's underlying member method code with example usage: obj.forward(pixel_values) and returns: features