docstr: labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
      Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
      config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
docstr_tok: ["labels","(","`","tf.Tensor","`","of","shape","`","(","batch_size",",",")","`",",","*","optional","*",")",":","Labels","for","computing","the","image","classification\/regression","loss",".","Indices","should","be","in","`","[","0",",","...",",","config.num_labels","-","1","]","`",".","If","`","config.num_labels",">","1","`","a","classification","loss","is","computed","(","Cross-Entropy",")","."]
code: def call(
      self,
      pixel_values: Optional[tf.Tensor] = None,
      labels: Optional[tf.Tensor] = None,
      output_hidden_states: Optional[bool] = None,
      return_dict: Optional[bool] = None,
      training: bool = False,
  ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
      r"""
      labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
          Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
          config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
      """
      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,
          output_hidden_states=output_hidden_states,
          return_dict=return_dict,
          training=training,
      )
  
      pooled_output = (
          outputs.pooler_output if return_dict else outputs[1]
      )
  
      flattened_output = self.classifier[0](pooled_output)
      logits = self.classifier[1](flattened_output)
  
      loss = (
          None
          if labels is None
          else self.hf_compute_loss(labels=labels, logits=logits)
      )
  
      if not return_dict:
          output = (logits,) + outputs[2:]
          return ((loss,) + output) if loss is not None else output
  
      return TFSequenceClassifierOutput(
          loss=loss, logits=logits, hidden_states=outputs.hidden_states
      )
  
imports: from typing import 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 TFRegNetForImageClassification obj's underlying member method code with example usage: obj.call(pixel_values, labels, output_hidden_states, return_dict, training) and returns: TFSequenceClassifierOutput, unknown