code: def test_SlateView(self) -> None:
      # Unit tests for the SlateView class
      sv = transforms.SlateView(keys=["a"], slate_size=-1)
  
      # GIVEN a SlateView with keys = ["a"]
      # WHEN data is passed in under a key "b"
      # THEN the value for "b" should not be unflattened since the key "b" is not in SlateView.keys!
      sv.slate_size = 1
      sv.keys = ["a"]
      a_in = torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]])
      b_in = torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]])
      data = {"a": a_in, "b": b_in}
      out = sv(data)
      self.assertEqual(out["b"].shape, torch.Size([4, 2]))
      self.assertTorchTensorEqual(out["b"], b_in)
  
      # GIVEN slate.size = 1 and keys = ["a", "b"]
      # WHEN input shape is [4, 2]
      # THEN output shape should be [4, 1, 2] for all keys
      sv.slate_size = 1
      sv.keys = ["a", "b"]
      a_in = torch.tensor([[1, 2], [3, 4], [5, 6], [7, 8]])
      b_in = torch.tensor([[10, 20], [30, 40], [50, 60], [70, 80]])
      data = {"a": a_in, "b": b_in}
      out = sv(data)
      a_out_412 = torch.tensor([[[1, 2]], [[3, 4]], [[5, 6]], [[7, 8]]])
      b_out_412 = torch.tensor(
          [[[10, 20]], [[30, 40]], [[50, 60]], [[70, 80]]]
      )
      self.assertEqual(out["a"].shape, torch.Size([4, 1, 2]))
      self.assertEqual(out["b"].shape, torch.Size([4, 1, 2]))
      self.assertDictOfTensorEqual(
          {"a": a_out_412, "b": b_out_412}, out
      )
  
      # GIVEN a SlateView with keys = ["a", "b"]
      # WHEN data is passed in missing one or more of those keys
      # THEN a KeyError should be raised
      sv.keys = ["a", "b"]
      a_in = torch.tensor([[1, 2], [3, 4]])
      c_in = torch.tensor([[1, 2], [3, 4]])
      data = {"a": a_in, "c": c_in}
      with self.assertRaises(KeyError):
          out = sv(data)
  
      # GIVEN a SlateView with keys = ["a"]
      # WHEN data is passed in that is of an invalid shape
      # THEN a RuntimeError should be raised
      sv.slate_size = 2
      sv.keys = ["a"]
      a_in = torch.tensor([[1, 2]])
      data = {"a": a_in}
      with self.assertRaises(RuntimeError):
          out = sv(data)
  
      # GIVEN slate.size = 2 and keys = ["a"]
      # WHEN input shape is [4, 3]
      # THEN output shape should be [2, 2, 3]
      sv.slate_size = 2
      sv.keys = ["a"]
      a_in = torch.tensor(
          [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
      )
      data = {"a": a_in}
      out = sv(data)
      a_out_223 = torch.tensor(
          [[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]
      )
      self.assertEqual(out["a"].shape, torch.Size([2, 2, 3]))
      self.assertDictOfTensorEqual({"a": a_out_223}, out)
  
imports: import os
  import unittest
  from copy import deepcopy
  from typing import List
  from unittest.mock import Mock, patch
  import numpy
  import reagent.core.types
  import torch
  from reagent.preprocessing import transforms
  from reagent.preprocessing.types import InputColumn
label_desc: Use image node_id 18 for calling the TestTransforms obj's underlying member method code with example usage: obj.test_SlateView() without return types