text: #
  #   Copyright 2021 Logical Clocks AB
  #
  #   Licensed under the Apache License, Version 2.0 (the "License");
  #   you may not use this file except in compliance with the License.
  #   You may obtain a copy of the License at
  #
  #       http://www.apache.org/licenses/LICENSE-2.0
  #
  #   Unless required by applicable law or agreed to in writing, software
  #   distributed under the License is distributed on an "AS IS" BASIS,
  #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  #   See the License for the specific language governing permissions and
  #   limitations under the License.
  #
  
  from hsml.utils.columnar_schema import ColumnarSchema
  from hsml.utils.tensor_schema import TensorSchema
  import numpy
  import pandas
  import json
  from typing import Optional, Union, TypeVar
  
  
  class Schema:
      """Create a schema for a model input or output.
  
      # Arguments
          object: The object to construct the schema from.
  
      # Returns
          `Schema`. The schema object.
      """
  
      def __init__(
          self,
          object: Optional[
              Union[
                  pandas.DataFrame,
                  pandas.Series,
                  TypeVar("pyspark.sql.dataframe.DataFrame"),  # noqa: F821
                  TypeVar("hsfs.training_dataset.TrainingDataset"),  # noqa: F821
                  numpy.ndarray,
                  list,
              ]
          ] = None,
      ):
          # A tensor schema is either ndarray of a list containing name, type and shape dicts
          if isinstance(object, numpy.ndarray) or (
              isinstance(object, list) and all(["shape" in entry for entry in object])
          ):
              self.tensor_schema = self._convert_tensor_to_schema(object).tensors
          else:
              self.columnar_schema = self._convert_columnar_to_schema(object).columns
  
      def _convert_columnar_to_schema(self, object):
          return ColumnarSchema(object)
  
      def _convert_tensor_to_schema(self, object):
          return TensorSchema(object)
  
      def json(self):
          return json.dumps(
              self, default=lambda o: getattr(o, "__dict__", o), sort_keys=True, indent=2
          )
  
      def to_dict(self):
          """
          Get dict representation of the Schema.
          """
          return json.loads(self.json())
  