class_docstr: Accepts parameters that specify an S3 input for an auto ml job
  
  Provides a method to turn those parameters into a dictionary.
docstr: Convert an S3 Uri or a list of S3 Uri to an AutoMLInput object.
  
  Args:
      inputs (str, list[str], PipelineVariable):
          a string or a list of string or a PipelineVariable that points to (a)
          S3 location(s) where input data is stored.
      target_attribute_name (str, PipelineVariable):
          the target attribute name for regression or classification.
      compression (str, PipelineVariable):
          if training data is compressed, the compression type. The default value is None.
      channel_type (str, PipelineVariable): The channel type an enum to specify
          whether the input resource is for training or validation.
          Valid values: training or validation.
      content_type (str, PipelineVariable):
          The content type of the data from the input source.
      s3_data_type (str, PipelineVariable): The data type for S3 data source.
          Valid values: ManifestFile or S3Prefix.
      sample_weight_attribute_name (str, PipelineVariable):
          the name of the dataset column representing sample weights
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code: def __init__(
      self,
      inputs,
      target_attribute_name,
      compression=None,
      channel_type=None,
      content_type=None,
      s3_data_type=None,
      sample_weight_attribute_name=None,
  ):
      """Convert an S3 Uri or a list of S3 Uri to an AutoMLInput object.
  
      Args:
          inputs (str, list[str], PipelineVariable):
              a string or a list of string or a PipelineVariable that points to (a)
              S3 location(s) where input data is stored.
          target_attribute_name (str, PipelineVariable):
              the target attribute name for regression or classification.
          compression (str, PipelineVariable):
              if training data is compressed, the compression type. The default value is None.
          channel_type (str, PipelineVariable): The channel type an enum to specify
              whether the input resource is for training or validation.
              Valid values: training or validation.
          content_type (str, PipelineVariable):
              The content type of the data from the input source.
          s3_data_type (str, PipelineVariable): The data type for S3 data source.
              Valid values: ManifestFile or S3Prefix.
          sample_weight_attribute_name (str, PipelineVariable):
              the name of the dataset column representing sample weights
      """
      self.inputs = inputs
      self.target_attribute_name = target_attribute_name
      self.compression = compression
      self.channel_type = channel_type
      self.content_type = content_type
      self.s3_data_type = s3_data_type
      self.sample_weight_attribute_name = sample_weight_attribute_name
  
imports: from __future__ import absolute_import
  import logging
  from typing import Optional, List, Dict
  from six import string_types
  from sagemaker import Model, PipelineModel, s3
  from sagemaker.automl.candidate_estimator import CandidateEstimator
  from sagemaker.config import AUTO_ML_ROLE_ARN_PATH, AUTO_ML_KMS_KEY_ID_PATH, AUTO_ML_VPC_CONFIG_PATH, AUTO_ML_VOLUME_KMS_KEY_ID_PATH, AUTO_ML_INTER_CONTAINER_ENCRYPTION_PATH
  from sagemaker.job import _Job
  from sagemaker.session import Session
  from sagemaker.utils import name_from_base, resolve_value_from_config, format_tags, Tags
  from sagemaker.workflow.entities import PipelineVariable
  from sagemaker.workflow.pipeline_context import runnable_by_pipeline
label_desc: Use image node_id 1 to create a new AutoMLInput object from inherited base classes: object with example: obj = AutoMLInput(inputs, target_attribute_name, compression, channel_type, content_type, s3_data_type, sample_weight_attribute_name)