class_docstr: This is the configuration class to store the configuration of a [`ConvNextModel`]. It is used to instantiate an
  ConvNeXT model according to the specified arguments, defining the model architecture. Instantiating a configuration
  with the defaults will yield a similar configuration to that of the ConvNeXT
  [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) architecture.
  
  Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
  documentation from [`PretrainedConfig`] for more information.
  
  Args:
      num_channels (`int`, *optional*, defaults to 3):
          The number of input channels.
      patch_size (`int`, optional, defaults to 4):
          Patch size to use in the patch embedding layer.
      num_stages (`int`, optional, defaults to 4):
          The number of stages in the model.
      hidden_sizes (`List[int]`, *optional*, defaults to [96, 192, 384, 768]):
          Dimensionality (hidden size) at each stage.
      depths (`List[int]`, *optional*, defaults to [3, 3, 9, 3]):
          Depth (number of blocks) for each stage.
      hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
          The non-linear activation function (function or string) in each block. If string, `"gelu"`, `"relu"`,
          `"selu"` and `"gelu_new"` are supported.
      initializer_range (`float`, *optional*, defaults to 0.02):
          The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
      layer_norm_eps (`float`, *optional*, defaults to 1e-12):
          The epsilon used by the layer normalization layers.
      layer_scale_init_value (`float`, *optional*, defaults to 1e-6):
          The initial value for the layer scale.
      drop_path_rate (`float`, *optional*, defaults to 0.0):
          The drop rate for stochastic depth.
  
  Example:
  ```python
  >>> from transformers import ConvNextConfig, ConvNextModel
  
  >>> # Initializing a ConvNext convnext-tiny-224 style configuration
  >>> configuration = ConvNextConfig()
  
  >>> # Initializing a model (with random weights) from the convnext-tiny-224 style configuration
  >>> model = ConvNextModel(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  ```
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code: def __init__(
      self,
      num_channels=3,
      patch_size=4,
      num_stages=4,
      hidden_sizes=None,
      depths=None,
      hidden_act="gelu",
      initializer_range=0.02,
      layer_norm_eps=1e-12,
      is_encoder_decoder=False,
      layer_scale_init_value=1e-6,
      drop_path_rate=0.0,
      image_size=224,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      self.num_channels = num_channels
      self.patch_size = patch_size
      self.num_stages = num_stages
      self.hidden_sizes = (
          [96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
      )
      self.depths = [3, 3, 9, 3] if depths is None else depths
      self.hidden_act = hidden_act
      self.initializer_range = initializer_range
      self.layer_norm_eps = layer_norm_eps
      self.layer_scale_init_value = layer_scale_init_value
      self.drop_path_rate = drop_path_rate
      self.image_size = image_size
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/convnext/configuration_convnext.py
imports: from collections import OrderedDict
  from typing import Mapping
  from packaging import version
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new ConvNextConfig object from inherited base classes: PretrainedConfig with example: obj = ConvNextConfig(num_channels, patch_size, num_stages, hidden_sizes, depths, hidden_act, initializer_range, layer_norm_eps, is_encoder_decoder, layer_scale_init_value, drop_path_rate, image_size)