code: def __init__(
      self,
      kernel_size,
      strides,
      expand_ratio,
      input_filters,
      output_filters,
      se_ratio,
      has_se,
      track_running_stats=True,
  ):
      oup = expand_ratio * input_filters
      if expand_ratio != 1:
          self._expand_conv = Tensor.glorot_uniform(
              oup, input_filters, 1, 1
          )
          self._bn0 = BatchNorm2d(
              oup, track_running_stats=track_running_stats
          )
      else:
          self._expand_conv = None
  
      self.strides = strides
      if strides == (2, 2):
          self.pad = [
              (kernel_size - 1) // 2 - 1,
              (kernel_size - 1) // 2,
          ] * 2
      else:
          self.pad = [(kernel_size - 1) // 2] * 4
  
      self._depthwise_conv = Tensor.glorot_uniform(
          oup, 1, kernel_size, kernel_size
      )
      self._bn1 = BatchNorm2d(
          oup, track_running_stats=track_running_stats
      )
  
      self.has_se = has_se
      if self.has_se:
          num_squeezed_channels = max(1, int(input_filters * se_ratio))
          self._se_reduce = Tensor.glorot_uniform(
              num_squeezed_channels, oup, 1, 1
          )
          self._se_reduce_bias = Tensor.zeros(num_squeezed_channels)
          self._se_expand = Tensor.glorot_uniform(
              oup, num_squeezed_channels, 1, 1
          )
          self._se_expand_bias = Tensor.zeros(oup)
  
      self._project_conv = Tensor.glorot_uniform(
          output_filters, oup, 1, 1
      )
      self._bn2 = BatchNorm2d(
          output_filters, track_running_stats=track_running_stats
      )
  
file_path: tinygrad/extra/models/efficientnet.py
imports: import math
  from tinygrad.tensor import Tensor
  from tinygrad.nn import BatchNorm2d
  from tinygrad.helpers import get_child, fetch
  from tinygrad.nn.state import torch_load
label_desc: Use image node_id 1 to create a new MBConvBlock object  with example: obj = MBConvBlock(kernel_size, strides, expand_ratio, input_filters, output_filters, se_ratio, has_se, track_running_stats)