class_docstr: LayerNorm that supports two data formats: channels_last (default) or channels_first.
  The ordering of the dimensions in the inputs. channels_last corresponds to inputs with shape (batch_size, height,
  width, channels) while channels_first corresponds to inputs with shape (batch_size, channels, height, width).
class_docstr_tok: ["LayerNorm","that","supports","two","data","formats",":","channels_last","(","default",")","or","channels_first",".","The","ordering","of","the","dimensions","in","the","inputs",".","channels_last","corresponds","to","inputs","with","shape","(","batch_size",",","height",",","width",",","channels",")","while","channels_first","corresponds","to","inputs","with","shape","(","batch_size",",","channels",",","height",",","width",")","."]
code: def __init__(
      self, normalized_shape, eps=1e-6, data_format="channels_last"
  ):
      super().__init__()
      self.weight = nn.Parameter(torch.ones(normalized_shape))
      self.bias = nn.Parameter(torch.zeros(normalized_shape))
      self.eps = eps
      self.data_format = data_format
      if self.data_format not in ["channels_last", "channels_first"]:
          raise NotImplementedError(
              f"Unsupported data format: {self.data_format}"
          )
      self.normalized_shape = (normalized_shape,)
  
imports: from typing import Optional, Tuple, Union
  import torch
  import torch.utils.checkpoint
  from torch import nn
  from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
  from ...activations import ACT2FN
  from ...modeling_outputs import BaseModelOutputWithNoAttention, BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
  from ...modeling_utils import PreTrainedModel
  from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
  from .configuration_convnext import ConvNextConfig
label_desc: Use image node_id 1 to create a new ConvNextLayerNorm object from inherited base classes: nn with example: obj = ConvNextLayerNorm(normalized_shape, eps, data_format)