class_docstr: Tests for methods in linear_interpolation module.
docstr: Tests linear interpolation with multiple batching dimensions.
docstr_tok: ["Tests","linear","interpolation","with","multiple","batching","dimensions","."]
code: def test_linear_interpolation_dynamic_shapes(self):
      """Tests linear interpolation with multiple batching dimensions."""
      dtype = np.float64
      x = [[[1.5, 2.0, 3.0], [3.5, 4.0, 2.0]]]
      x_data = [[1, 2], [3, 4]]
      y_data = [[[0, 1], [2, 3]]]
  
      # Define function with generic dynamic inputs
      @tf.function(
          input_signature=[
              tf.TensorSpec([None, None, None], dtype=dtype),
              tf.TensorSpec([None, None], dtype=dtype),
              tf.TensorSpec([None, None, None], dtype=dtype),
          ]
      )
      def interpolate(x, x_data, y_data):
          return tff.math.interpolation.linear.interpolate(
              x, x_data, y_data
          )
  
      results = interpolate(x, x_data, y_data)
      self.assertAllClose(
          results, np.array([[[0.5, 1.0, 1.0], [2.5, 3.0, 2.0]]]), 1e-8
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  import tf_quant_finance
  from tensorflow.python.framework import test_util
label_desc: Use image node_id 14 for calling the LinearInterpolation obj's underlying member method code with example usage: obj.test_linear_interpolation_dynamic_shapes() and returns: tff