class_docstr: FeatureGroup definition.
  
  This class instantiates a FeatureGroup object that comprises of a name for the FeatureGroup,
  session instance, and a list of feature definition objects i.e., FeatureDefinition.
  
  Attributes:
      name (str): name of the FeatureGroup instance.
      sagemaker_session (Session): session instance to perform boto calls.
          If None, a new Session will be created.
      feature_definitions (Sequence[FeatureDefinition]): list of FeatureDefinitions.
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docstr: Load feature definitions from a Pandas DataFrame.
  
  Column name is used as feature name. Feature type is inferred from the dtype
  of the column. Dtype :literal:`int_`, int8, int16, int32, int64, uint8, uint16, uint32
  and uint64 are mapped to Integral feature type. Dtype :literal:`float_`, float16, float32
  and float64 are mapped to Fractional feature type. string dtype is mapped to
  String feature type.
  
  No feature definitions will be loaded if the given data_frame contains
  unsupported dtypes.
  
  Args:
      data_frame (DataFrame):
  
  Returns:
      list of FeatureDefinition
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code: def load_feature_definitions(
      self,
      data_frame: DataFrame,
  ) -> Sequence[FeatureDefinition]:
      """Load feature definitions from a Pandas DataFrame.
  
      Column name is used as feature name. Feature type is inferred from the dtype
      of the column. Dtype :literal:`int_`, int8, int16, int32, int64, uint8, uint16, uint32
      and uint64 are mapped to Integral feature type. Dtype :literal:`float_`, float16, float32
      and float64 are mapped to Fractional feature type. string dtype is mapped to
      String feature type.
  
      No feature definitions will be loaded if the given data_frame contains
      unsupported dtypes.
  
      Args:
          data_frame (DataFrame):
  
      Returns:
          list of FeatureDefinition
      """
      feature_definitions = []
      for column in data_frame:
          feature_type = self.DTYPE_TO_FEATURE_DEFINITION_CLS_MAP.get(
              str(data_frame[column].dtype).lower(), None
          )
          if feature_type:
              feature_definitions.append(
                  FeatureDefinition(
                      feature_name=column, feature_type=feature_type
                  )
              )
          else:
              raise ValueError(
                  f"Failed to infer Feature type based on dtype {data_frame[column].dtype} "
                  f"for column {column}."
              )
      self.feature_definitions = feature_definitions
      return self.feature_definitions
  
imports: from __future__ import absolute_import
  import copy
  import logging
  import math
  import os
  import tempfile
  from concurrent.futures import as_completed
  from concurrent.futures import ThreadPoolExecutor
  from typing import Optional, Sequence, List, Dict, Any, Union
  from urllib.parse import urlparse
  from multiprocessing.pool import AsyncResult
  import signal
  import attr
  import pandas
  from pandas import DataFrame
  import boto3
  from botocore.config import Config
  from pathos.multiprocessing import ProcessingPool
  from sagemaker.config import FEATURE_GROUP_ROLE_ARN_PATH, FEATURE_GROUP_OFFLINE_STORE_KMS_KEY_ID_PATH, FEATURE_GROUP_ONLINE_STORE_KMS_KEY_ID_PATH
  from sagemaker.session import Session
  from sagemaker.feature_store.feature_definition import FeatureDefinition, FeatureTypeEnum
  from sagemaker.feature_store.inputs import OnlineStoreConfig, OnlineStoreSecurityConfig, S3StorageConfig, OfflineStoreConfig, DataCatalogConfig, FeatureValue, FeatureParameter, TableFormatEnum, DeletionModeEnum, TtlDuration, OnlineStoreConfigUpdate, OnlineStoreStorageTypeEnum
  from sagemaker.utils import resolve_value_from_config, format_tags, Tags
label_desc: Use image node_id 9 for calling the FeatureGroup obj's underlying member method code with example usage: obj.load_feature_definitions(data_frame) and returns: self