class_docstr: This corresponds to the final phase of each block in the original implementation.
class_docstr_tok: ["This","corresponds","to","the","final","phase","of","each","block","in","the","original","implementation","."]
code: def __init__(
      self,
      config: EfficientNetConfig,
      in_dim: int,
      out_dim: int,
      stride: int,
      drop_rate: float,
      id_skip: bool,
  ):
      super().__init__()
      self.apply_dropout = stride == 1 and not id_skip
      self.project_conv = nn.Conv2d(
          in_channels=in_dim,
          out_channels=out_dim,
          kernel_size=1,
          padding="same",
          bias=False,
      )
      self.project_bn = nn.BatchNorm2d(
          num_features=out_dim,
          eps=config.batch_norm_eps,
          momentum=config.batch_norm_momentum,
      )
      self.dropout = nn.Dropout(p=drop_rate)
  
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 EfficientNetFinalBlockLayer object from inherited base classes: nn with example: obj = EfficientNetFinalBlockLayer(config, in_dim, out_dim, stride, drop_rate, id_skip)