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
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docstr: Creates a TensorFlow `SparseTensor` from the inputs
  
  Args:
      inputs (list): Two input tensors contining
          matrix indices (size 1 x E x 2) of type int64, and
          matrix values (size (size 1 x E),
          where E is the number of non-zero entries in the matrix.
  
  Returns:
      TensorFlow SparseTensor that represents the converted sparse matrix.
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code: def call(self, inputs):
      """
      Creates a TensorFlow `SparseTensor` from the inputs
  
      Args:
          inputs (list): Two input tensors contining
              matrix indices (size 1 x E x 2) of type int64, and
              matrix values (size (size 1 x E),
              where E is the number of non-zero entries in the matrix.
  
      Returns:
          TensorFlow SparseTensor that represents the converted sparse matrix.
      """
      # Here we squeeze the specified axis
      if self.axis is not None:
          indices = K.squeeze(inputs[0], self.axis)
          values = K.squeeze(inputs[1], self.axis)
      else:
          indices = inputs[0]
          values = inputs[1]
  
      if self.dtype is not None:
          values = K.cast(values, self.dtype)
  
      # Import tensorflow here so that the backend check will work without
      # tensorflow installed.
      import tensorflow as tf
  
      # Build sparse tensor for the matrix
      output = tf.SparseTensor(
          indices=indices, values=values, dense_shape=self.matrix_shape
      )
      return output
  
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 4 for calling the SqueezedSparseConversion obj's underlying member method code with example usage: obj.call(inputs) and returns: output