class_docstr: ResNet Embeddings (stem) composed of a single aggressive convolution.
class_docstr_tok: ["ResNet","Embeddings","(","stem",")","composed","of","a","single","aggressive","convolution","."]
code: def __init__(self, config: ResNetConfig, **kwargs) -> None:
      super().__init__(**kwargs)
      self.embedder = TFResNetConvLayer(
          config.num_channels,
          config.embedding_size,
          kernel_size=7,
          stride=2,
          activation=config.hidden_act,
          name="embedder",
      )
      self.pooler = tf.keras.layers.MaxPool2D(
          pool_size=3, strides=2, padding="valid", name="pooler"
      )
      self.num_channels = config.num_channels
  
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 1 to create a new TFResNetEmbeddings object from inherited base classes: tf.keras.layers with example: obj = TFResNetEmbeddings(config)