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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def production_variant(model_name, instance_type, initial_instance_count=1, variant_name='AllTraffic', initial_weight=1, accelerator_type=None):
 """Create a production variant description suitable for use in a ``ProductionVariant`` list as part of a ``CreateEndpointConfig`` request. Args: model_name (str):
 The name of the SageMaker model this production variant references. instance_type (str):
 The EC2 instance type for this production variant. For example, 'ml.c4.8xlarge'. initial_instance_count (int):
 The initial instance count for this production variant (default: 1). variant_name (string):
 The ``VariantName`` of this production variant (default: 'AllTraffic'). initial_weight (int):
 The relative ``InitialVariantWeight`` of this production variant (default: 1). accelerator_type (str):
 Type of Elastic Inference accelerator for this production variant. For example, 'ml.eia1.medium'. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html Returns: dict[str, str]: An SageMaker ``ProductionVariant`` description """

    production_variant_configuration = {
        'ModelName': model_name,
        'InstanceType': instance_type,
        'InitialInstanceCount': initial_instance_count,
        'VariantName': variant_name,
        'InitialVariantWeight': initial_weight
    }

    if accelerator_type:
        production_variant_configuration['AcceleratorType'] = accelerator_type

    return production_variant_configuration