class_docstr: Linear Multistep Scheduler for discrete beta schedules. Based on the original k-diffusion implementation by
  Katherine Crowson:
  https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181
  
  [`~ConfigMixin`] takes care of storing all config attributes that are passed in the scheduler's `__init__`
  function, such as `num_train_timesteps`. They can be accessed via `scheduler.config.num_train_timesteps`.
  [`SchedulerMixin`] provides general loading and saving functionality via the [`SchedulerMixin.save_pretrained`] and
  [`~SchedulerMixin.from_pretrained`] functions.
  
  Args:
      num_train_timesteps (`int`): number of diffusion steps used to train the model.
      beta_start (`float`): the starting `beta` value of inference.
      beta_end (`float`): the final `beta` value.
      beta_schedule (`str`):
          the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
          `linear` or `scaled_linear`.
      trained_betas (`jnp.ndarray`, optional):
          option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
      prediction_type (`str`, default `epsilon`, optional):
          prediction type of the scheduler function, one of `epsilon` (predicting the noise of the diffusion
          process), `sample` (directly predicting the noisy sample`) or `v_prediction` (see section 2.4
          https://imagen.research.google/video/paper.pdf)
      dtype (`jnp.dtype`, *optional*, defaults to `jnp.float32`):
          the `dtype` used for params and computation.
code: def create_state(
      self, common: Optional[CommonSchedulerState] = None
  ) -> LMSDiscreteSchedulerState:
      if common is None:
          common = CommonSchedulerState.create(self)
  
      timesteps = jnp.arange(
          0, self.config.num_train_timesteps
      ).round()[::-1]
      sigmas = (
          (1 - common.alphas_cumprod) / common.alphas_cumprod
      ) ** 0.5
  
      # standard deviation of the initial noise distribution
      init_noise_sigma = sigmas.max()
  
      return LMSDiscreteSchedulerState.create(
          common=common,
          init_noise_sigma=init_noise_sigma,
          timesteps=timesteps,
          sigmas=sigmas,
      )
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple, Union
  import flax
  import jax.numpy
  from scipy import integrate
  from ..configuration_utils import ConfigMixin, register_to_config
  from .scheduling_utils_flax import CommonSchedulerState, FlaxKarrasDiffusionSchedulers, FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
label_desc: Use image node_id 3 for calling the FlaxLMSDiscreteScheduler obj's underlying member method code with example usage: obj.create_state(common) and returns: LMSDiscreteSchedulerState