class_docstr: Converts Keras tensors containing indices and values to a tensorflow sparse
  tensor. The input tensors are expected to have a batch dimension of 1 which
  will be removed before conversion to a matrix.
  
  This only works with a tensorflow Keras backend.
  
  Example:
      ```
      A_indices = Input(batch_shape=(1, None, 2), dtype="int64")
      A_values = Input(batch_shape=(1, None))
      Ainput = TFSparseConversion(shape=(N, N))([A_indices, A_values])
      ```
  
  Args:
      shape (list of int): The shape of the sparse matrix to create
      dtype (str or tf.dtypes.DType): Data type for the created sparse matrix
class_docstr_tok: ["Converts","Keras","tensors","containing","indices","and","values","to","a","tensorflow","sparse","tensor",".","The","input","tensors","are","expected","to","have","a","batch","dimension","of","1","which","will","be","removed","before","conversion","to","a","matrix",".","This","only","works","with","a","tensorflow","Keras","backend",".","Example",":","``","`","A_indices","=","Input","(","batch_shape=","(","1",",","None",",","2",")",",","dtype=","''","int64","''",")","A_values","=","Input","(","batch_shape=","(","1",",","None",")",")","Ainput","=","TFSparseConversion","(","shape=","(","N",",","N",")",")","(","[","A_indices",",","A_values","]",")","``","`","Args",":","shape","(","list","of","int",")",":","The","shape","of","the","sparse","matrix","to","create","dtype","(","str","or","tf.dtypes.DType",")",":","Data","type","for","the","created","sparse","matrix"]
code: def get_config(self):
      config = {"shape": self.matrix_shape, "dtype": self.dtype}
      return config
  
file_path: stellargraph/stellargraph/layer/misc.py
imports: import tensorflow
  from tensorflow.keras.layers import Layer
  from tensorflow.keras import backend
  import warnings
label_desc: Use image node_id 2 for calling the SqueezedSparseConversion obj's underlying member method code with example usage: obj.get_config() and returns: config