class_docstr: Grid-to-Grid Model with U-Net skip connections.
class_docstr_tok: ["Grid-to-Grid","Model","with","U-Net","skip","connections","."]
docstr: Forward method.
  
  Args:
    voxgrid: `[batch, inres, inres, inres, nc]` tensor, input voxel grid.
    pts: `[batch, num_points, 3]` tensor, input query points.
    training: bool, flag indicating whether model is in training mode.
  Returns:
    vals: `[batch, num_points, 3]` tensor, predicted values at query points.
docstr_tok: ["Forward","method",".","Args",":","voxgrid",":","`","[","batch",",","inres",",","inres",",","inres",",","nc","]","`","tensor",",","input","voxel","grid",".","pts",":","`","[","batch",",","num_points",",","3","]","`","tensor",",","input","query","points",".","training",":","bool",",","flag","indicating","whether","model","is","in","training","mode",".","Returns",":","vals",":","`","[","batch",",","num_points",",","3","]","`","tensor",",","predicted","values","at","query","points","."]
code: def call(self, voxgrid, pts, training=False):
      """Forward method.
  
      Args:
        voxgrid: `[batch, inres, inres, inres, nc]` tensor, input voxel grid.
        pts: `[batch, num_points, 3]` tensor, input query points.
        training: bool, flag indicating whether model is in training mode.
      Returns:
        vals: `[batch, num_points, 3]` tensor, predicted values at query points.
      """
      self.outgrid = self.unet(voxgrid, training=training)
      val = self.lig(self.outgrid, pts, training=training)
      return val
  
imports: import math
  import tensorflow.compat.v1
  from tensorflow_graphics.projects.local_implicit_grid.core import local_implicit_grid_layer
label_desc: Use image node_id 2 for calling the ModelG2G obj's underlying member method code with example usage: obj.call(voxgrid, pts, training) and returns: val