code: def fold(
      input, output_size, kernel_size, dilation=1, padding=0, stride=1
  ):
      orig_ndim = input.ndim
      if orig_ndim == 2:
          input = ivy.expand_dims(input, axis=0)
      elif orig_ndim != 3:
          raise ivy.utils.exceptions.IvyException(
              "only 2D or batched 3D inputs are supported"
          )
      stride = [stride] * 2 if isinstance(stride, int) else stride
      dilation = (
          [dilation] * 2 if isinstance(dilation, int) else dilation
      )
      padding = [padding] * 2 if isinstance(padding, int) else padding
      kernel_size = (
          [kernel_size] * 2
          if isinstance(kernel_size, int)
          else kernel_size
      )
      output_size = (
          [output_size] * 2
          if isinstance(output_size, int)
          else output_size
      )
      input_shape = [
          (
              output_size[i]
              + 2 * padding[i]
              - dilation[i] * (kernel_size[i] - 1)
              - 1
          )
          // stride[i]
          + 1
          for i in range(2)
      ]
      n_batches = input.shape[0]
      n_channels = input.shape[1] // math.prod(kernel_size)
      output = ivy.zeros(
          (n_batches, n_channels, *output_size), dtype=input.dtype
      )
      output_padded = ivy.zero_pad(
          output,
          ((0, 0), (0, 0), (padding[0],) * 2, (padding[1],) * 2),
      )
      k = 0
      for i in range(input_shape[0]):
          for j in range(input_shape[1]):
              i_in = i * stride[0]
              j_in = j * stride[1]
              patch = input[:, :, k].reshape(
                  (n_batches, n_channels, *kernel_size)
              )
              output_padded[
                  :,
                  :,
                  i_in : i_in
                  + kernel_size[0] * dilation[0] : dilation[0],
                  j_in : j_in
                  + kernel_size[1] * dilation[1] : dilation[1],
              ] += patch
              k += 1
      ret = ivy.array(
          output_padded[
              :,
              :,
              padding[0] : output_padded.shape[2] - padding[0],
              padding[1] : output_padded.shape[3] - padding[1],
          ]
      )
      if orig_ndim == 2:
          return ivy.squeeze(ret, axis=0)
      return ret
  
file_path: ivy/ivy/functional/frontends/torch/nn/functional/convolution_functions.py
imports: import math
  import ivy
  from ivy.func_wrapper import with_unsupported_dtypes
  from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
label_desc: Use image node_id 10 for calling a global function with example usage: fold(input, output_size, kernel_size, dilation, padding, stride) and returns: ret, ivy