docstr: Create a new Transform3d representing 3D translations.
  
  Option I: Translate(xyz, dtype=torch.float32, device='cpu')
      xyz should be a tensor of shape (N, 3)
  
  Option II: Translate(x, y, z, dtype=torch.float32, device='cpu')
      Here x, y, and z will be broadcast against each other and
      concatenated to form the translation. Each can be:
          - A python scalar
          - A torch scalar
          - A 1D torch tensor
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code: def __init__(
      self,
      x,
      y=None,
      z=None,
      dtype: torch.dtype = torch.float32,
      device: Optional[Device] = None,
  ) -> None:
      """
      Create a new Transform3d representing 3D translations.
  
      Option I: Translate(xyz, dtype=torch.float32, device='cpu')
          xyz should be a tensor of shape (N, 3)
  
      Option II: Translate(x, y, z, dtype=torch.float32, device='cpu')
          Here x, y, and z will be broadcast against each other and
          concatenated to form the translation. Each can be:
              - A python scalar
              - A torch scalar
              - A 1D torch tensor
      """
      xyz = _handle_input(x, y, z, dtype, device, "Translate")
      super().__init__(device=xyz.device, dtype=dtype)
      N = xyz.shape[0]
  
      mat = torch.eye(4, dtype=dtype, device=self.device)
      mat = mat.view(1, 4, 4).repeat(N, 1, 1)
      mat[:, 3, :3] = xyz
      self._matrix = mat
  
imports: import math
  import os
  import warnings
  from typing import List, Optional, Union
  import torch
  from ..common.datatypes import Device, get_device, make_device
  from ..common.workaround import _safe_det_3x3
  from .rotation_conversions import _axis_angle_rotation
  from .se3 import se3_log_map
label_desc: Use image node_id 1 to create a new Translate object from inherited base classes: Transform3d with example: obj = Translate(x, y, z, dtype, device)