code: def affine_grid(data, target_shape):
      """affine_grid operator that generates 2D sampling grid.
  
      This operation is described in https://arxiv.org/pdf/1506.02025.pdf. It generates a uniform
      sampling grid within the target shape and normalizes it to [-1, 1]. The provided affine
      transformation is then applied on the sampling grid.
  
      Parameters
      ----------
      data : tvm.Tensor
          3-D with shape [batch, 2, 3]. The affine matrix.
  
      target_shape: list/tuple of two int
          Specifies the output shape (H, W).
  
      Returns
      -------
      Output : tvm.Tensor
          4-D with shape [batch, 2, target_height, target_width]
      """
      assert target_shape is not None
      assert len(target_shape) == 2
      assert (
          target_shape[0] > 1 and target_shape[1] > 1
      ), "target height/width should be greater than 1"
  
      dtype = data.dtype
      y_step = tir.const(
          (2.0 - 1e-7) / (target_shape[0] - 1), dtype=dtype
      )
      x_step = tir.const(
          (2.0 - 1e-7) / (target_shape[1] - 1), dtype=dtype
      )
      start = tir.const(-1.0, dtype=dtype)
  
      def _compute(n, dim, i, j):
          y = start + i * y_step
          x = start + j * x_step
          return (
              data[n, dim, 0] * x
              + data[n, dim, 1] * y
              + data[n, dim, 2]
          )
  
      oshape = (data.shape[0], len(target_shape), *target_shape)
      return te.compute(oshape, _compute, tag="affine_grid")
  
imports: from tvm import te, tir
label_desc: Use image node_id 1 for calling a global function with example usage: affine_grid(data, target_shape) and returns: te, unknown