class_docstr: This class is comparable to (and inspired by) the SwinEmbeddings class
  found in src/transformers/models/swin/modeling_swin.py.
class_docstr_tok: ["This","class","is","comparable","to","(","and","inspired","by",")","the","SwinEmbeddings","class","found","in","src\/transformers\/models\/swin\/modeling_swin.py","."]
code: def build(self, input_shape=None):
      if self.built:
          return
      self.built = True
      if getattr(self, "patch_embeddings", None) is not None:
          with tf.name_scope(self.patch_embeddings.name):
              self.patch_embeddings.build(
                  [None, None, None, self.config.num_channels]
              )
      if getattr(self, "layernorm", None) is not None:
          with tf.name_scope(self.layernorm.name):
              self.layernorm.build(
                  [None, None, None, self.config.hidden_sizes[0]]
              )
  
imports: from __future__ import annotations
  from typing import List, Optional, Tuple, Union
  import numpy
  import tensorflow
  from ...activations_tf import get_tf_activation
  from ...modeling_tf_outputs import TFBaseModelOutputWithNoAttention, TFBaseModelOutputWithPooling, TFBaseModelOutputWithPoolingAndNoAttention, TFImageClassifierOutputWithNoAttention
  from ...modeling_tf_utils import TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, 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_convnextv2 import ConvNextV2Config
label_desc: Use image node_id 3 for calling the TFConvNextV2Embeddings obj's underlying member method code with example usage: obj.build(input_shape) without return types