class_docstr: ## Base class for sampling algorithms
docstr: :param model: is the model to predict noise $\epsilon_  ext{cond}(x_t, c)$
code: def __init__(self, model: LatentDiffusion):
      """
      :param model: is the model to predict noise $\epsilon_\text{cond}(x_t, c)$
      """
      super().__init__()
      # Set the model $\epsilon_\text{cond}(x_t, c)$
      self.model = model
      # Get number of steps the model was trained with $T$
      self.n_steps = model.n_steps
  
file_path: annotated_deep_learning_paper_implementations/labml_nn/diffusion/stable_diffusion/sampler/__init__.py
imports: from typing import Optional, List
  import torch
  from labml_nn.diffusion.stable_diffusion.latent_diffusion import LatentDiffusion
label_desc: Use image node_id 1 to create a new DiffusionSampler object  with example: obj = DiffusionSampler(model)