class_docstr: A class for interacting with CreateAutoMLJob API.
docstr: Convert inputs to AutoML InputDataConfig.
  
  Args:
      inputs (str, list[str]): local path(s) or S3 uri(s) of input datasets.
      validate_uri (bool): indicates whether it is needed to validate S3 uri.
      compression (str): Compression type of the input data.
      target_attribute_name (str): the target attribute name for classification
          or regression.
  
  Returns (dict): a dict of AutoML InputDataConfig
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code: def _format_inputs_to_input_config(
      cls,
      inputs,
      validate_uri=True,
      compression=None,
      target_attribute_name=None,
      content_type=None,
      s3_data_type=None,
      sample_weight_attribute_name=None,
  ):
      """Convert inputs to AutoML InputDataConfig.
  
      Args:
          inputs (str, list[str]): local path(s) or S3 uri(s) of input datasets.
          validate_uri (bool): indicates whether it is needed to validate S3 uri.
          compression (str): Compression type of the input data.
          target_attribute_name (str): the target attribute name for classification
              or regression.
  
      Returns (dict): a dict of AutoML InputDataConfig
      """
      if inputs is None:
          return None
  
      channels = []
      if isinstance(inputs, AutoMLInput):
          channels.append(inputs.to_request_dict())
      elif isinstance(inputs, string_types):
          channel = _Job._format_string_uri_input(
              inputs,
              validate_uri,
              compression=compression,
              target_attribute_name=target_attribute_name,
          ).config
          if content_type is not None:
              channel["ContentType"] = content_type
          if s3_data_type is not None:
              channel["DataSource"]["S3DataSource"][
                  "S3DataType"
              ] = s3_data_type
          if sample_weight_attribute_name is not None:
              channel[
                  "SampleWeightAttributeName"
              ] = sample_weight_attribute_name
          channels.append(channel)
      elif isinstance(inputs, list):
          for input_entry in inputs:
              channel = _Job._format_string_uri_input(
                  input_entry,
                  validate_uri,
                  compression=compression,
                  target_attribute_name=target_attribute_name,
              ).config
              if content_type is not None:
                  channel["ContentType"] = content_type
              if s3_data_type is not None:
                  channel["DataSource"]["S3DataSource"][
                      "S3DataType"
                  ] = s3_data_type
              if sample_weight_attribute_name is not None:
                  channel[
                      "SampleWeightAttributeName"
                  ] = sample_weight_attribute_name
              channels.append(channel)
      else:
          msg = (
              "Cannot format input {}. Expecting a string or "
              "a list of strings or a list of AutoMLInputs."
          )
          raise ValueError(msg.format(inputs))
  
      for channel in channels:
          if channel["TargetAttributeName"] is None:
              raise ValueError("TargetAttributeName cannot be None.")
  
      return channels
  
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 5 for calling the AutoMLJob obj's underlying member method code with example usage: obj._format_inputs_to_input_config(cls, inputs, validate_uri, compression, target_attribute_name, content_type, s3_data_type, sample_weight_attribute_name) and returns: channels, None