for (int32_t i = start; i < end; i += step) {
    (*data)[i] = i;
  }
}

// Fills out_dims with the dimensions of the given tensor.
void GetDims(const TF_Tensor* t, int64_t* out_dims) {
  int num_dims = TF_NumDims(t);
  for (int i = 0; i < num_dims; i++) {
    out_dims[i] = TF_Dim(t, i);
  }
}

// Runs model as is if output is a scalar,
// else sums the output tensor before returning.

  TF_Tensor* analytical_tensor;
  auto s = GetValue(t, &analytical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  int64_t num_elem_analytical = 1;
  auto num_dims_analytical = TF_NumDims(analytical_tensor);
  ASSERT_EQ(dims.size(), num_dims_analytical);
  for (int j = 0; j < num_dims_analytical; j++) {
    auto dim_analytical = TF_Dim(analytical_tensor, j);
    ASSERT_EQ(dims[j], dim_analytical);

  ASSERT_EQ(TF_OK, TF_GetCode(s_)) << TF_Message(s_);
  ASSERT_NE(nullptr, value);
  EXPECT_EQ(TF_INT8, TF_TensorType(value));
  EXPECT_EQ(ndims, TF_NumDims(value));
  for (int i = 0; i < TF_NumDims(value); ++i) {
    EXPECT_EQ(dims[i], TF_Dim(value, i)) << i;
  }
  EXPECT_EQ(sizeof(char) * TF_ARRAYSIZE(tensor), TF_TensorByteSize(value));

    for (int i = 0; i < expected_results.size(); ++i) {
      TF_Tensor* out = csession.output_tensor(i);
      ASSERT_TRUE(out != nullptr);
      EXPECT_EQ(TF_INT32, TF_TensorType(out));
      EXPECT_EQ(1, TF_NumDims(out));
      CompareInt32Tensor(expected_results[i], out);
    }
  }

  // Run the host graph, which now contains a function and check that
  // outputs are as expected.