class_docstr: The `EncoderTiny` layer is a simpler version of the `Encoder` layer.
  
  Args:
      in_channels (`int`):
          The number of input channels.
      out_channels (`int`):
          The number of output channels.
      num_blocks (`Tuple[int, ...]`):
          Each value of the tuple represents a Conv2d layer followed by `value` number of `AutoencoderTinyBlock`'s to
          use.
      block_out_channels (`Tuple[int, ...]`):
          The number of output channels for each block.
      act_fn (`str`):
          The activation function to use. See `~diffusers.models.activations.get_activation` for available options.
docstr: The forward method of the `EncoderTiny` class.
docstr_tok: ["The","forward","method","of","the","`","EncoderTiny","`","class","."]
code: def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:
      r"""The forward method of the `EncoderTiny` class."""
      if self.training and self.gradient_checkpointing:
  
          def create_custom_forward(module):
              def custom_forward(*inputs):
                  return module(*inputs)
  
              return custom_forward
  
          if is_torch_version(">=", "1.11.0"):
              x = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(self.layers),
                  x,
                  use_reentrant=False,
              )
          else:
              x = torch.utils.checkpoint.checkpoint(
                  create_custom_forward(self.layers), x
              )
  
      else:
          # scale image from [-1, 1] to [0, 1] to match TAESD convention
          x = self.layers(x.add(1).div(2))
  
      return x
  
imports: from dataclasses import dataclass
  from typing import Optional, Tuple
  import numpy
  import torch
  import torch.nn
  from ...utils import BaseOutput, is_torch_version
  from ...utils.torch_utils import randn_tensor
  from ..activations import get_activation
  from ..attention_processor import SpatialNorm
  from ..unet_2d_blocks import AutoencoderTinyBlock, UNetMidBlock2D, get_down_block, get_up_block
label_desc: Use image node_id 2 for calling the EncoderTiny obj's underlying member method code with example usage: obj.forward(x) and returns: x, custom_forward, module