class_docstr: Compute the Sobel operator and returns the magnitude per channel.
  
  Args:
      normalized: if True, L1 norm of the kernel is set to 1.
      eps: regularization number to avoid NaN during backprop.
  
  Return:
      the sobel edge gradient magnitudes map.
  
  Shape:
      - Input: :math:`(B, C, H, W)`
      - Output: :math:`(B, C, H, W)`
  
  Examples:
      >>> input = torch.rand(1, 3, 4, 4)
      >>> output = Sobel()(input)  # 1x3x4x4
class_docstr_tok: ["Compute","the","Sobel","operator","and","returns","the","magnitude","per","channel",".","Args",":","normalized",":","if","True",",","L1","norm","of","the","kernel","is","set","to","1.","eps",":","regularization","number","to","avoid","NaN","during","backprop",".","Return",":","the","sobel","edge","gradient","magnitudes","map",".","Shape",":","-","Input",":",":","math",":","`","(","B",",","C",",","H",",","W",")","`","-","Output",":",":","math",":","`","(","B",",","C",",","H",",","W",")","`","Examples",":",">",">",">","input","=","torch.rand","(","1",",","3",",","4",",","4",")",">",">",">","output","=","Sobel","(",")","(","input",")","#","1x3x4x4"]
code: def __repr__(self) -> str:
      return f"{self.__class__.__name__}(normalized={self.normalized})"
  
imports: from __future__ import annotations
  import torch
  import torch.nn.functional
  from kornia.core import Module, Tensor, pad
  from kornia.core.check import KORNIA_CHECK_IS_TENSOR, KORNIA_CHECK_SHAPE
  from .kernels import get_spatial_gradient_kernel2d, get_spatial_gradient_kernel3d, normalize_kernel2d
label_desc: Use image node_id 2 for calling the Sobel obj's underlying member method code with example usage: obj.__repr__() and returns: self, str, self, str