class_docstr: Dataclass that configures most of the hyperparameters used for GBM model training.
class_docstr_tok: ["Dataclass","that","configures","most","of","the","hyperparameters","used","for","GBM","model","training","."]
code: def can_tune_batch_size(self) -> bool:
      return False
  
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 1 for calling the GBMTrainerConfig obj's underlying member method code with example usage: obj.can_tune_batch_size() and returns: False