class_docstr: Placeholder docstring
class_docstr_tok: ["Placeholder","docstring"]
docstr: A collection of Amazon :class:~`Record` objects serialized and stored in S3.
  
  Args:
      s3_data (str or PipelineVariable): The S3 location of the training data
      num_records (int): The number of records in the set.
      feature_dim (int): The dimensionality of "values" arrays in the
          Record features, and label (if each Record is labeled).
      s3_data_type (str or PipelineVariable): Valid values: 'S3Prefix', 'ManifestFile'.
          If 'S3Prefix', ``s3_data`` defines a prefix of s3 objects to train
          on. All objects with s3 keys beginning with ``s3_data`` will be
          used to train. If 'ManifestFile', then ``s3_data`` defines a
          single s3 manifest file, listing each s3 object to train on.
      channel (str or PipelineVariable): The SageMaker Training Job channel this RecordSet
          should be bound to
code: def __init__(
      self,
      s3_data: Union[str, PipelineVariable],
      num_records: int,
      feature_dim: int,
      s3_data_type: Union[str, PipelineVariable] = "ManifestFile",
      channel: Union[str, PipelineVariable] = "train",
  ):
      """A collection of Amazon :class:~`Record` objects serialized and stored in S3.
  
      Args:
          s3_data (str or PipelineVariable): The S3 location of the training data
          num_records (int): The number of records in the set.
          feature_dim (int): The dimensionality of "values" arrays in the
              Record features, and label (if each Record is labeled).
          s3_data_type (str or PipelineVariable): Valid values: 'S3Prefix', 'ManifestFile'.
              If 'S3Prefix', ``s3_data`` defines a prefix of s3 objects to train
              on. All objects with s3 keys beginning with ``s3_data`` will be
              used to train. If 'ManifestFile', then ``s3_data`` defines a
              single s3 manifest file, listing each s3 object to train on.
          channel (str or PipelineVariable): The SageMaker Training Job channel this RecordSet
              should be bound to
      """
      self.s3_data = s3_data
      self.feature_dim = feature_dim
      self.num_records = num_records
      self.s3_data_type = s3_data_type
      self.channel = channel
  
imports: from __future__ import absolute_import
  import json
  import logging
  import tempfile
  from typing import Union, Optional, Dict
  from six.moves.urllib.parse import urlparse
  from sagemaker import image_uris, s3_utils
  from sagemaker.amazon import validation
  from sagemaker.amazon.hyperparameter import Hyperparameter
  from sagemaker.amazon.common import write_numpy_to_dense_tensor
  from sagemaker.deprecations import renamed_warning
  from sagemaker.estimator import EstimatorBase, _TrainingJob
  from sagemaker.inputs import FileSystemInput, TrainingInput
  from sagemaker.utils import sagemaker_timestamp, check_and_get_run_experiment_config
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
  from sagemaker.workflow import is_pipeline_variable
label_desc: Use image node_id 1 to create a new RecordSet object from inherited base classes: object with example: obj = RecordSet(s3_data, num_records, feature_dim, s3_data_type, channel)