code: def test_squeezedsparseconversion_dtype():
      N = 10
      x_t = keras.Input(batch_shape=(1, N, 1), dtype="float64")
      A_ind = keras.Input(batch_shape=(1, None, 2), dtype="int64")
      A_val = keras.Input(batch_shape=(1, None), dtype="float32")
  
      A_mat = SqueezedSparseConversion(shape=(N, N), dtype="float64")(
          [A_ind, A_val]
      )
  
      x_out = keras.layers.Lambda(
          lambda xin: K.expand_dims(
              K.dot(xin[0], K.squeeze(xin[1], 0)), 0
          )
      )([A_mat, x_t])
  
      model = keras.Model(inputs=[x_t, A_ind, A_val], outputs=x_out)
  
      x = np.random.randn(1, N, 1)
      A_indices, A_values, A = sparse_matrix_example(N)
  
      z = model.predict(
          [x, np.expand_dims(A_indices, 0), np.expand_dims(A_values, 0)]
      )
  
      assert A_mat.dtype == tf.dtypes.float64
      np.testing.assert_allclose(
          z.squeeze(), A.dot(x.squeeze()), atol=1e-7, rtol=1e-5
      )
  
file_path: stellargraph/tests/layer/test_misc.py
imports: import tensorflow
  from tensorflow import keras
  import numpy
  import scipy.sparse
  import pytest
  from stellargraph.layer.misc import *
  from ..test_utils.graphs import create_graph_features, example_graph
  from stellargraph.mapper import *
  from stellargraph.layer import *
label_desc: Use image node_id 3 for calling a global function with example usage: test_squeezedsparseconversion_dtype() without return types