code: def __init__(self, config: RegNetConfig, **kwargs):
      super().__init__(**kwargs)
      self.stages = list()
      # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
      self.stages.append(
          TFRegNetStage(
              config,
              config.embedding_size,
              config.hidden_sizes[0],
              stride=2 if config.downsample_in_first_stage else 1,
              depth=config.depths[0],
              name="stages.0",
          )
      )
      in_out_channels = zip(
          config.hidden_sizes, config.hidden_sizes[1:]
      )
      for i, ((in_channels, out_channels), depth) in enumerate(
          zip(in_out_channels, config.depths[1:])
      ):
          self.stages.append(
              TFRegNetStage(
                  config,
                  in_channels,
                  out_channels,
                  depth=depth,
                  name=f"stages.{i+1}",
              )
          )
  
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 TFRegNetEncoder object from inherited base classes: tf.keras.layers with example: obj = TFRegNetEncoder(config)