code: def call(
      self,
      pixel_values: tf.Tensor,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      training=False,
  ) -> Union[
      TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]
  ]:
      output_hidden_states = (
          output_hidden_states
          if output_hidden_states is not None
          else self.config.output_hidden_states
      )
      return_dict = (
          return_dict
          if return_dict is not None
          else self.config.use_return_dict
      )
  
      outputs = self.regnet(
          pixel_values=pixel_values,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
      if not return_dict:
          return (outputs[0],) + outputs[1:]
  
      return TFBaseModelOutputWithPoolingAndNoAttention(
          last_hidden_state=outputs.last_hidden_state,
          pooler_output=outputs.pooler_output,
          hidden_states=outputs.hidden_states,
      )
  
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 2 for calling the TFRegNetModel obj's underlying member method code with example usage: obj.call(pixel_values, output_hidden_states, return_dict, training) and returns: TFBaseModelOutputWithPoolingAndNoAttention, unknown