class_docstr: RegNet Embedddings (stem) composed of a single aggressive convolution.
class_docstr_tok: ["RegNet","Embedddings","(","stem",")","composed","of","a","single","aggressive","convolution","."]
code: def __init__(self, config: RegNetConfig):
      super().__init__()
      self.embedder = RegNetConvLayer(
          config.num_channels,
          config.embedding_size,
          kernel_size=3,
          stride=2,
          activation=config.hidden_act,
      )
      self.num_channels = config.num_channels
  
imports: from typing import Optional
  import torch
  import torch.utils.checkpoint
  from torch import Tensor, nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_outputs import BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 1 to create a new RegNetEmbeddings object from inherited base classes: nn with example: obj = RegNetEmbeddings(config)