class_docstr: Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
  References:
      (1) github.com:rwightman/pytorch-image-models
code: def __init__(self, drop_path: float, **kwargs):
      super().__init__(**kwargs)
      self.drop_path = drop_path
  
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 TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput
  from ...modeling_tf_utils import TFModelInputType, TFPreTrainedModel, TFSequenceClassificationLoss, get_initializer, keras_serializable, unpack_inputs
  from ...tf_utils import shape_list
  from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  from .configuration_convnext import ConvNextConfig
label_desc: Use image node_id 1 to create a new TFConvNextDropPath object from inherited base classes: tf.keras.layers with example: obj = TFConvNextDropPath(drop_path)