Pruned efficientnets don't respect the `in_chans` parameter

[jphdotam]

When creating a model using timm.create_model(arch, pretrained=True, in_chans=1, num_classes=1), single-channel input images can be used with tf_efficientnet_b2_ns, but not efficientnet_b3_pruned. The pruned models result in the following error:

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/timm/models/efficientnet.py", line 557, in forward
    x = self.forward_features(x)

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/timm/models/efficientnet.py", line 540, in forward_features
    x = self.conv_stem(x)

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1482, in _call_impl
    return forward_call(*args, **kwargs)

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/timm/models/layers/conv2d_same.py", line 30, in forward
    return conv2d_same(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)

  File "/home/james/miniconda3/envs/mammo/lib/python3.10/site-packages/timm/models/layers/conv2d_same.py", line 17, in conv2d_same
    return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)

RuntimeError: Given groups=1, weight of size [40, 3, 3, 3], expected input[14, 1, 2459, 2459] to have 3 channels, but got 1 channels instead