code: def get_llm_trainer_conds():
      """Returns a JSON schema of conditionals to validate against adapter types."""
      conds = []
      for trainer in _llm_trainer_schema_registry:
          trainer_cls = _llm_trainer_schema_registry[trainer]
          other_props = (
              schema_utils.unload_jsonschema_from_marshmallow_class(
                  trainer_cls
              )["properties"]
          )
          schema_utils.remove_duplicate_fields(other_props)
          preproc_cond = schema_utils.create_cond(
              {"type": trainer},
              other_props,
          )
          conds.append(preproc_cond)
      return conds
  
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 6 for calling a global function with example usage: get_llm_trainer_conds() and returns: conds