class_docstr: RepMLP Block.
  
  Args:
      input_channels (`int`):
          Number of input channels & final output channels.
      internal_neurons (`int`):
          Number of channels used to connect conv2d layers inside block.
      h (`int`):
          Input image height.
      w (`int`):
          Input image weight.
      reparam_conv_k (`tuple or list`):
          Numbers of conv layers.
      globalperceptron_reduce (`int`):
          Number to reduce internal hidden channels.
      num_sharesets (`int`):
          Number of sharesets.
      deploy (`bool`):
          Flag to control deploy parameters like bias.
  
  Example:
      >>> from towhee.models.repmlp import RepMLPBlock
      >>> import torch
      >>>
      >>> data = torch.rand(1, 4, 6, 6)
      >>> model = RepMLPBlock(in_channels=4, out_channels=4, h=3, w=3)
      >>> outs = model(data)
      >>> print(outs.shape)
      torch.Size([1, 4, 6, 6])
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code: def partition_affine(self, x, h_parts, w_parts):
      fc_inputs = x.reshape(-1, self.share_s * self.h * self.w, 1, 1)
      out = self.fc3(fc_inputs)
      out = out.reshape(-1, self.share_s, self.h, self.w)
      out = self.fc3_bn(out)
      out = out.reshape(
          -1, h_parts, w_parts, self.share_s, self.h, self.w
      )
      return out
  
file_path: towhee/towhee/models/repmlp/blocks.py
imports: import torch
  from torch import nn
  from towhee.models.layers.conv_bn_activation import Conv2dBNActivation
  from towhee.models.utils.fuse_bn import fuse_bn
label_desc: Use image node_id 3 for calling the RepMLPBlock obj's underlying member method code with example usage: obj.partition_affine(x, h_parts, w_parts) and returns: out