public CreateAutoMLJobRequest withInputDataConfig(AutoMLChannel... inputDataConfig) {
        if (this.inputDataConfig == null) {
            setInputDataConfig(new java.util.ArrayList<AutoMLChannel>(inputDataConfig.length));
        }
        for (AutoMLChannel ele : inputDataConfig) {
            this.inputDataConfig.add(ele);
        }
        return this;
    }
<p>
An array of channel objects that describes the input data and its location. Each channel is a named input source.
Similar to <code>InputDataConfig</code> supported by <a
href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html"
>HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required
for the training dataset. There is not a minimum number of rows required for the validation dataset.
</p>
<p>
<b>NOTE:</b> This method appends the values to the existing list (if any). Use
{@link #setInputDataConfig(java.util.Collection)} or {@link #withInputDataConfig(java.util.Collection)} if you
want to override the existing values.
</p>

@param inputDataConfig
       An array of channel objects that describes the input data and its location. Each channel is a named input
       source. Similar to <code>InputDataConfig</code> supported by <a
       href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html"
       >HyperParameterTrainingJobDefinition</a>. Format(s) supported: CSV, Parquet. A minimum of 500 rows is
       required for the training dataset. There is not a minimum number of rows required for the validation
       dataset.
@return Returns a reference to this object so that method calls can be chained together.
aws-java-sdk-sagemaker/src/main/java/com/amazonaws/services/sagemaker/model/CreateAutoMLJobRequest.java