code: def call(
      self,
      pixel_values: tf.Tensor,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      training: bool = False,
  ) -> Union[
      Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention
  ]:
      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
      )
  
      resnet_outputs = self.resnet(
          pixel_values=pixel_values,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
      return resnet_outputs
  
imports: from typing import Optional, Tuple, Union
  import tensorflow
  from ...activations_tf import ACT2FN
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPoolingAndNoAttention, TFImageClassifierOutputWithNoAttention
  from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_resnet import ResNetConfig
label_desc: Use image node_id 2 for calling the TFResNetModel obj's underlying member method code with example usage: obj.call(pixel_values, output_hidden_states, return_dict, training) and returns: resnet_outputs