class_docstr: Creates a network that has data dependent shapes.
class_docstr_tok: ["Creates","a","network","that","has","data","dependent","shapes","."]
code: def GraphFn(self, x):
      # With the unique() op we create a tensor with data dependent shape.
      x = math_ops.floor(x * 10)
      y, idx = array_ops.unique(x)
      y = y * 2 + y
  
      # The rest is only needed to ensure that the output has the same size as
      # the input (expected by the test harness).
      padding = array_ops.constant([0])
      n = array_ops.shape(x) - array_ops.shape(y)
      padding = array_ops.concat([padding, n], 0)
      padding = array_ops.expand_dims(padding, 0)
      y = array_ops.pad(y, padding)
  
      return array_ops.identity(y, name="output_0")
  
file_path: tensorflow/tensorflow/python/compiler/tensorrt/test/data_dependent_shape_test.py
imports: import os
  from unittest import SkipTest
  from tensorflow.python.compiler.tensorrt.test import tf_trt_integration_test_base
  from tensorflow.python.framework import dtypes
  from tensorflow.python.ops import array_ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 1 for calling the TrtModeTestBase obj's underlying member method code with example usage: obj.GraphFn(x) and returns: array_ops