class_docstr: This corresponds to the expansion phase of each block in the original implementation.
class_docstr_tok: ["This","corresponds","to","the","expansion","phase","of","each","block","in","the","original","implementation","."]
code: def forward(self, hidden_states: torch.FloatTensor) -> torch.Tensor:
      # Expand phase
      hidden_states = self.expand_conv(hidden_states)
      hidden_states = self.expand_bn(hidden_states)
      hidden_states = self.expand_act(hidden_states)
  
      return hidden_states
  
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 EfficientNetExpansionLayer obj's underlying member method code with example usage: obj.forward(hidden_states) and returns: hidden_states