class_docstr: A class of MBConv: Mobile Inverted Residual Bottleneck.
  
  Attributes:
    endpoints: dict. A list of internal tensors.
class_docstr_tok: ["A","class","of","MBConv",":","Mobile","Inverted","Residual","Bottleneck",".","Attributes",":","endpoints",":","dict",".","A","list","of","internal","tensors","."]
docstr: Initializes a MBConv block.
  
  Args:
    block_args: BlockArgs, arguments to create a Block.
    global_params: GlobalParams, a set of global parameters.
    name: layer name.
docstr_tok: ["Initializes","a","MBConv","block",".","Args",":","block_args",":","BlockArgs",",","arguments","to","create","a","Block",".","global_params",":","GlobalParams",",","a","set","of","global","parameters",".","name",":","layer","name","."]
code: def __init__(self, block_args, global_params, name=None):
      """Initializes a MBConv block.
  
      Args:
        block_args: BlockArgs, arguments to create a Block.
        global_params: GlobalParams, a set of global parameters.
        name: layer name.
      """
      super().__init__(name=name)
  
      self._block_args = block_args
      self._global_params = global_params
      self._local_pooling = global_params.local_pooling
      self._batch_norm_momentum = global_params.batch_norm_momentum
      self._batch_norm_epsilon = global_params.batch_norm_epsilon
      self._batch_norm = global_params.batch_norm
      self._condconv_num_experts = global_params.condconv_num_experts
      self._data_format = global_params.data_format
      self._channel_axis = (
          1 if self._data_format == "channels_first" else -1
      )
  
      self._relu_fn = global_params.relu_fn or tf.nn.swish
      self._has_se = (
          global_params.use_se
          and self._block_args.se_ratio is not None
          and 0 < self._block_args.se_ratio <= 1
      )
  
      self._clip_projection_output = (
          global_params.clip_projection_output
      )
  
      self.endpoints = None
  
      if self._block_args.condconv:
          raise ValueError("Condconv is not supported.")
  
      # Builds the block accordings to arguments.
      self._build()
  
imports: import collections
  import itertools
  import math
  from absl import logging
  import numpy
  import six
  from six.moves import xrange
  import tensorflow
  import utils
label_desc: Use image node_id 1 to create a new MBConvBlock object from inherited base classes: tf.keras.layers with example: obj = MBConvBlock(block_args, global_params, name)