code: def _apply_adagrad(r, t, x, g, h, norm_coefficient, epsilon, decay_factor):  # type: ignore
      # Compute adjusted learning-rate.
      r_ = r / (1 + t * decay_factor)
      # Add gradient of regularization term.
      g_regularized = norm_coefficient * x + g
      # Update squared accumulated gradient.
      h_new = h + g_regularized * g_regularized
      # Compute ADAGRAD's gradient scaling factors
      h_sqrt = np.sqrt(h_new) + epsilon
      # Apply ADAGRAD update rule.
      x_new = x - r_ * g_regularized / h_sqrt
      return (x_new, h_new)
  
imports: import numpy
  from onnx.reference.ops.aionnx_preview_training._op_run_training import OpRunTraining
label_desc: Use image node_id 1 for calling a global function with example usage: _apply_adagrad(r, t, x, g, h, norm_coefficient, epsilon, decay_factor) and returns: x_new,  h_new