docstr: Initialize this model class
  Parameters:
      opt -- training/test options
  A few things can be done here.
  - (required) call the initialization function of BaseModel
  - define loss function, visualization images, model names, and optimizers
docstr_tok: ["Initialize","this","model","class","Parameters",":","opt","--","training\/test","options","A","few","things","can","be","done","here",".","-","(","required",")","call","the","initialization","function","of","BaseModel","-","define","loss","function",",","visualization","images",",","model","names",",","and","optimizers"]
code: def __init__(self, option, model_type, dataset, modules):
      """
      Initialize this model class
      Parameters:
          opt -- training/test options
      A few things can be done here.
      - (required) call the initialization function of BaseModel
      - define loss function, visualization images, model names, and optimizers
      """
  
      BackboneBasedModel.__init__(
          self, option, model_type, dataset, modules
      )
      self.set_last_mlp(option.mlp_cls)
      self.loss_names = ["loss_reg"]
  
imports: import logging
  import torch
  import torch.nn.functional
  from typing import Any
  from torch_points3d.datasets.multiscale_data import MultiScaleBatch
  from torch_points3d.models.base_architectures import BackboneBasedModel
  from torch_points3d.models.base_model import BaseModel
  from torch_points3d.core.common_modules.dense_modules import Conv1D
  from torch_points3d.core.common_modules.base_modules import Seq
label_desc: Use image node_id 1 to create a new PatchSiamese object from inherited base classes: BackboneBasedModel with example: obj = PatchSiamese(option, model_type, dataset, modules)