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).
  
  Args:
      normalized_shape (`int`): input shape from an expected input size
      eps (`float`): a value added to the denominator for numerical stability
      data_format (`str`): either "channels_last" or "channels_first"  to differ input shapes
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",")",".","Args",":","normalized_shape","(","`","int","`",")",":","input","shape","from","an","expected","input","size","eps","(","`","float","`",")",":","a","value","added","to","the","denominator","for","numerical","stability","data_format","(","`","str","`",")",":","either","``","channels_last","''","or","``","channels_first","''","to","differ","input","shapes"]
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
      self.normalized_shape = (normalized_shape,)
  
file_path: towhee/towhee/models/convnext/utils.py
imports: import torch
  from torch import nn
  from towhee.models.layers.droppath import DropPath
label_desc: Use image node_id 1 to create a new LayerNorm object from inherited base classes: nn with example: obj = LayerNorm(normalized_shape, eps, data_format)