class_docstr: Translate the tensor in pixel units.
  
  Args:
      translation: tensor containing the amount of pixels to
        translate in the x and y direction. The tensor must have a shape of
        (B, 2), where B is batch size, last dimension contains dx dy.
      mode: interpolation mode to calculate output values
        ``'bilinear'`` | ``'nearest'``.
      padding_mode: padding mode for outside grid values
        ``'zeros'`` | ``'border'`` | ``'reflection'``.
      align_corners: interpolation flag.
  
  Returns:
      The translated tensor with the same shape as the input.
  
  Example:
      >>> img = torch.rand(1, 3, 4, 4)
      >>> translation = torch.tensor([[1., 0.]])
      >>> out = Translate(translation)(img)
      >>> print(out.shape)
      torch.Size([1, 3, 4, 4])
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code: def __init__(
      self,
      translation: torch.Tensor,
      mode: str = "bilinear",
      padding_mode: str = "zeros",
      align_corners: bool = True,
  ) -> None:
      super().__init__()
      self.translation: torch.Tensor = translation
      self.mode: str = mode
      self.padding_mode: str = padding_mode
      self.align_corners: bool = align_corners
  
imports: from typing import Optional, Tuple, Union
  import torch
  from torch import nn
  from kornia.core import ones, ones_like, zeros
  from kornia.filters import gaussian_blur2d
  from kornia.utils import _extract_device_dtype
  from kornia.utils.image import perform_keep_shape_image
  from kornia.utils.misc import eye_like
  from .imgwarp import get_affine_matrix2d, get_projective_transform, get_rotation_matrix2d, warp_affine, warp_affine3d
label_desc: Use image node_id 1 to create a new Translate object from inherited base classes: nn with example: obj = Translate(translation, mode, padding_mode, align_corners)