class_docstr: A classic ResNet's residual layer composed by two `3x3` convolutions.
class_docstr_tok: ["A","classic","ResNet","'s","residual","layer","composed","by","two","`","3x3","`","convolutions","."]
code: def call(
      self, hidden_state: tf.Tensor, training: bool = False
  ) -> tf.Tensor:
      residual = hidden_state
      hidden_state = self.conv1(hidden_state, training=training)
      hidden_state = self.conv2(hidden_state, training=training)
      residual = self.shortcut(residual, training=training)
      hidden_state += residual
      hidden_state = self.activation(hidden_state)
      return hidden_state
  
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 TFResNetBasicLayer obj's underlying member method code with example usage: obj.call(hidden_state, training) and returns: hidden_state