class_docstr: Base class for different image classification models
class_docstr_tok: ["Base","class","for","different","image","classification","models"]
docstr: Create dummy input and labels for CI/CD purposes. Child classes must override it
  if functionality is different.
docstr_tok: ["Create","dummy","input","and","labels","for","CI\/CD","purposes",".","Child","classes","must","override","it","if","functionality","is","different","."]
code: def dummy_input_and_label(self, batch_size: int) -> Dict:
      """Create dummy input and labels for CI/CD purposes. Child classes must override it
      if functionality is different.
      """
      img_channels = 3
      if is_test_env():
          # We use smaller spatial resolution, for faster testing.
          # We use 32 because most ImageNet models do a down-sampling by a factor of 32 before
          # global average pooling and classification layer.
          height = 32
          width = 32
      else:
          # this is the typical resolution used in ImageNet datasets
          height = 224
          width = 224
      n_labels = 10
      img_tensor = torch.randn(
          batch_size, img_channels, height, width, dtype=torch.float
      )
      label_tensor = torch.randint(
          low=0, high=n_labels, size=(batch_size,)
      ).long()
      return {"samples": img_tensor, "targets": label_tensor}
  
imports: import argparse
  from typing import Any, Dict, List, Mapping, Optional, Tuple
  import torch
  from torch import Tensor, nn
  from torch.utils.checkpoint import checkpoint
  from common import is_test_env
  from cvnets.layers import LinearLayer
  from cvnets.misc.init_utils import initialize_fc_layer
  from cvnets.models import MODEL_REGISTRY, BaseAnyNNModel
  from cvnets.neural_augmentor import build_neural_augmentor
  from utils import logger
label_desc: Use image node_id 12 for calling the BaseImageEncoder obj's underlying member method code with example usage: obj.dummy_input_and_label(batch_size) and returns: dict