@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format('batch_size, sequence_length'))
@add_code_sample_docstrings(processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, **kwargs):
    '\n        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):\n            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,\n            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\n            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\n        '
    inputs = input_processing(func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, labels=labels, training=training, kwargs_call=kwargs)
    outputs = self.convbert(inputs['input_ids'], attention_mask=inputs['attention_mask'], token_type_ids=inputs['token_type_ids'], position_ids=inputs['position_ids'], head_mask=inputs['head_mask'], inputs_embeds=inputs['inputs_embeds'], output_attentions=inputs['output_attentions'], output_hidden_states=inputs['output_hidden_states'], return_dict=inputs['return_dict'], training=inputs['training'])
    logits = self.classifier(outputs[0], training=inputs['training'])
    loss = (None if (inputs['labels'] is None) else self.compute_loss(inputs['labels'], logits))
    if (not inputs['return_dict']):
        output = ((logits,) + outputs[1:])
        return (((loss,) + output) if (loss is not None) else output)
    return TFSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
    Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
@add_start_docstrings_to_model_forward(CONVBERT_INPUTS_DOCSTRING.format('batch_size, sequence_length'))
@add_code_sample_docstrings(processor_class=_TOKENIZER_FOR_DOC, checkpoint=_CHECKPOINT_FOR_DOC, output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(self, input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, output_attentions=None, output_hidden_states=None, return_dict=None, labels=None, training=False, **kwargs):
    '\n        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):\n            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,\n            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\n            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\n        '
    inputs = input_processing(func=self.call, config=self.config, input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, labels=labels, training=training, kwargs_call=kwargs)
    outputs = self.convbert(inputs['input_ids'], attention_mask=inputs['attention_mask'], token_type_ids=inputs['token_type_ids'], position_ids=inputs['position_ids'], head_mask=inputs['head_mask'], inputs_embeds=inputs['inputs_embeds'], output_attentions=inputs['output_attentions'], output_hidden_states=inputs['output_hidden_states'], return_dict=inputs['return_dict'], training=inputs['training'])
    logits = self.classifier(outputs[0], training=inputs['training'])
    loss = (None if (inputs['labels'] is None) else self.compute_loss(inputs['labels'], logits))
    if (not inputs['return_dict']):
        output = ((logits,) + outputs[1:])
        return (((loss,) + output) if (loss is not None) else output)
    return TFSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)