docstr: Generate constraints the given node:
  Currently supported operations:
  - Reshape
  - Add
  - conv2d
docstr_tok: ["Generate","constraints","the","given","node",":","Currently","supported","operations",":","-","Reshape","-","Add","-","conv2d"]
code: def generate_constraints_node(self, n: Node, counter):
      """
      Generate constraints the given node:
      Currently supported operations:
      - Reshape
      - Add
      - conv2d
      """
  
      if n.op == "placeholder":
          x, counter = gen_tvar(counter)
          self.symbol_dict[n] = x
  
          my_type = n.type
  
          if n.type != Dyn and (not isinstance(n.type, TensorType)):
              if n.type == torch.nn.parameter.Parameter:
                  # since we have a parameter, the shape must be static
                  assert "example_value" in n.meta
                  my_type = TensorType(n.meta["example_value"].size())
              else:
                  my_type = Dyn
  
          c1 = BinConstraintT(my_type, x, op_precision)
          c2 = BinConstraintT(x, MAX_TENSOR_RANK, op_leq)
          return [c1, c2], counter
  
      elif n.op == "call_function":
          if n.target in _INFERENCE_RULES:
              return _INFERENCE_RULES[n.target](
                  n, self.symbol_dict, self.constraints, counter
              )
          else:
              raise RuntimeError(
                  f"No inference rule registered for target {n.target}!"
              )
  
      elif n.op == "call_module":
          module_instance = self.traced.get_submodule(n.target)
          if type(module_instance) in _INFERENCE_RULES:
              return _INFERENCE_RULES[type(module_instance)](
                  n,
                  module_instance,
                  self.symbol_dict,
                  self.constraints,
                  counter,
              )
          else:
              raise RuntimeError(
                  f"No inference rule registered for class {type(module_instance)}!"
              )
  
      elif n.op == "call_method":
          if n.target in _INFERENCE_RULES:
              return _INFERENCE_RULES[n.target](
                  n, self.symbol_dict, self.constraints, counter
              )
          else:
              raise RuntimeError(
                  f"No inference rule registered for target {n.target}!"
              )
  
      elif n.op == "get_attr":
          t = self.traced_params.get(n.target, None)
  
          if isinstance(t, torch.Tensor):
              if len(t.shape) > 0:
                  res = list(t.shape)
                  attr_type = TensorType(res)
                  output, counter = gen_tvar(counter)
                  self.symbol_dict[n] = output
                  return [
                      BinConstraintT(output, attr_type, op_eq)
                  ], counter
              else:
                  # scalar?
                  return [], counter
          else:
              return [], counter
  
      elif n.op == "output":
          return [], counter
  
      else:
          raise NotImplementedError(
              f"Method {n.op} not yet implemented"
          )
  
imports: import torch
  import operator
  import warnings
  from typing import Callable, Dict, Iterable
  from torch.fx._symbolic_trace import _assert_is_none
  from torch.fx.experimental.migrate_gradual_types.constraint import ApplyBroadcasting, CalcProduct, Disj, TGreatestUpperBound, CalcMaxPool, CalcConv, Conj, BinConstraintT, CanReshape, BinConstraintD, GetItem, T, F, TVar, DVar, GetItemTensor, IndexSelect, Transpose, DGreatestUpperBound
  from torch.fx.experimental.migrate_gradual_types.operation import op_eq, op_matching, op_consistency, op_leq, op_precision, op_gt, op_div, op_sub, op_neq, op_lt, op_add, op_mul
  from torch.fx.node import Target, Node
  from torch.fx.experimental.migrate_gradual_types.util import gen_tensor_dims, gen_nat_constraints, gen_dvar, gen_tvar, gen_bvar
  from torch.fx.tensor_type import Dyn, TensorType
  from torch.nn.modules.conv import Conv2d
  from torch.nn.modules.batchnorm import BatchNorm2d
label_desc: Use image node_id 3 for calling the ConstraintGenerator obj's underlying member method code with example usage: obj.generate_constraints_node(n, counter) and returns: list,  counter, _INFERENCE_RULES, _INFERENCE_RULES, _INFERENCE_RULES, list,  counter, list,  counter, list,  counter, list,  counter