parameters: (feature_columns)
return_statement: return placeholders
docstring: Returns placeholder tensors for inference.
  
    Args:
      feature_columns: An iterable containing all the feature columns. All items
        should be instances of classes derived from _FeatureColumn.
    Returns:
      A dict mapping feature keys to SparseTensors (sparse columns) or
      placeholder Tensors (dense columns).
docstring_summary: Returns placeholder tensors for inference.
docstring_tokens: Returns placeholder tensors for inference .
function: def make_place_holder_tensors_for_base_features(feature_columns):
    """Returns placeholder tensors for inference.
  
    Args:
      feature_columns: An iterable containing all the feature columns. All items
        should be instances of classes derived from _FeatureColumn.
    Returns:
      A dict mapping feature keys to SparseTensors (sparse columns) or
      placeholder Tensors (dense columns).
    """
    # Get dict mapping features to FixedLenFeature or VarLenFeature values.
    dict_for_parse_example = create_feature_spec_for_parsing(feature_columns)
    placeholders = {}
    for column_name, column_type in dict_for_parse_example.items():
      if isinstance(column_type, parsing_ops.VarLenFeature):
        # Sparse placeholder for sparse tensors.
        placeholders[column_name] = array_ops.sparse_placeholder(
            column_type.dtype,
            name="Placeholder_" + column_name)
      else:
        # Simple placeholder for dense tensors.
        placeholders[column_name] = array_ops.placeholder(
            column_type.dtype,
            shape=(None, column_type.shape[0]),
            name="Placeholder_" + column_name)
    return placeholders
function_tokens: def make_place_holder_tensors_for_base_features ( feature_columns ) : # Get dict mapping features to FixedLenFeature or VarLenFeature values. dict_for_parse_example = create_feature_spec_for_parsing ( feature_columns ) placeholders = { } for column_name , column_type in dict_for_parse_example . items ( ) : if isinstance ( column_type , parsing_ops . VarLenFeature ) : # Sparse placeholder for sparse tensors. placeholders [ column_name ] = array_ops . sparse_placeholder ( column_type . dtype , name = "Placeholder_" + column_name ) else : # Simple placeholder for dense tensors. placeholders [ column_name ] = array_ops . placeholder ( column_type . dtype , shape = ( None , column_type . shape [ 0 ] ) , name = "Placeholder_" + column_name ) return placeholders