class_docstr: RegNet Embeddings (stem) composed of a single aggressive convolution.
code: def __init__(self, config: RegNetConfig, **kwargs):
      super().__init__(**kwargs)
      self.num_channels = config.num_channels
      self.embedder = TFRegNetConvLayer(
          out_channels=config.embedding_size,
          kernel_size=3,
          stride=2,
          activation=config.hidden_act,
          name="embedder",
      )
  
imports: from typing import Dict, Optional, Tuple, Union
  import tensorflow
  from ...activations_tf import ACT2FN
  from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import logging
  from .configuration_regnet import RegNetConfig
label_desc: Use image node_id 1 to create a new TFRegNetEmbeddings object from inherited base classes: tf.keras.layers with example: obj = TFRegNetEmbeddings(config)