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 call(
      self, pixel_values: tf.Tensor, training: bool = False
  ) -> tf.Tensor:
      _, _, _, num_channels = shape_list(pixel_values)
      if tf.executing_eagerly() and num_channels != self.num_channels:
          raise ValueError(
              "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
          )
      hidden_state = pixel_values
      hidden_state = self.embedder(hidden_state)
      hidden_state = tf.pad(
          hidden_state, [[0, 0], [1, 1], [1, 1], [0, 0]]
      )
      hidden_state = self.pooler(hidden_state)
      return hidden_state
  
imports: from typing import Dict, 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 TFResNetEmbeddings obj's underlying member method code with example usage: obj.call(pixel_values, training) and returns: hidden_state