class_docstr: Dataclass that configures most of the hyperparameters used for ECD model training.
class_docstr_tok: ["Dataclass","that","configures","most","of","the","hyperparameters","used","for","ECD","model","training","."]
code: def update_batch_size_grad_accum(self, num_workers: int):
      from ludwig.utils.trainer_utils import (
          get_rendered_batch_size_grad_accum,
      )
  
      (
          self.batch_size,
          self.gradient_accumulation_steps,
      ) = get_rendered_batch_size_grad_accum(self, num_workers)
  
imports: from abc import ABC
  from typing import Optional, Type, Union
  import torch
  from packaging.version import parse
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import AUTO, EFFECTIVE_BATCH_SIZE, LOSS, MAX_BATCH_SIZE, MAX_POSSIBLE_BATCH_SIZE, MODEL_ECD, MODEL_GBM, MODEL_LLM, TRAINING
  from ludwig.error import ConfigValidationError
  from ludwig.schema import utils
  from ludwig.schema.lr_scheduler import LRSchedulerConfig, LRSchedulerDataclassField
  from ludwig.schema.metadata import TRAINER_METADATA
  from ludwig.schema.optimizers import BaseOptimizerConfig, GradientClippingConfig, GradientClippingDataclassField, OptimizerDataclassField
  from ludwig.schema.profiler import ProfilerConfig, ProfilerDataclassField
  from ludwig.schema.utils import ludwig_dataclass
  from ludwig.utils.registry import Registry
label_desc: Use image node_id 2 for calling the ECDTrainerConfig obj's underlying member method code with example usage: obj.update_batch_size_grad_accum(num_workers) without return types