code: def resize_mat(self, x, t: int):
      B, C, block_size, block_size1 = x.shape
      _assert(block_size == block_size1, "")
      if t <= 1:
          return x
      x = x.view(B * C, -1, 1, 1)
      x = x * torch.eye(t, t, dtype=x.dtype, device=x.device)
      x = x.view(B * C, block_size, block_size, t, t)
      x = torch.cat(torch.split(x, 1, dim=1), dim=3)
      x = torch.cat(torch.split(x, 1, dim=2), dim=4)
      x = x.view(B, C, block_size * t, block_size * t)
      return x
  
file_path: pytorch-image-models/timm/layers/non_local_attn.py
imports: import torch
  from torch import nn
  from torch.nn import functional
  from .conv_bn_act import ConvNormAct
  from .helpers import make_divisible
  from .trace_utils import _assert
label_desc: Use image node_id 2 for calling the BilinearAttnTransform obj's underlying member method code with example usage: obj.resize_mat(x, t) and returns: x, x