
"""simple docstring"""

def 							A		(       snake_case   :str       ,      snake_case   :Union[str, Any]	)   ->			Optional[Any]:
 # "extended trapezoidal rule"
 # int(f) = dx/2 * (f1 + 2f2 + ... + fn)
 __UpperCamelCase       						= (boundary[1] - boundary[0]) / steps
 __UpperCamelCase       						= boundary[0]
 __UpperCamelCase       						= boundary[1]
 __UpperCamelCase       						= make_points(snake_case       ,      snake_case       ,      snake_case	)
 __UpperCamelCase       						= 0.0
 y += (h / 2.0) * f(snake_case	)
 for i in x_i:
  # print(i)
  y += h * f(snake_case	)
 y += (h / 2.0) * f(snake_case	)
 return y






def 							A		(       snake_case   :Tuple       ,      snake_case   :int       ,      snake_case   :Optional[Any]	)   ->			Optional[Any]:
 __UpperCamelCase       						= a + h
 while x < (b - h):
  yield x
  __UpperCamelCase       						= x + h






def 							A		(       snake_case   :Optional[int]	)   ->			Optional[int]:  # enter your function here
 __UpperCamelCase       						= (x - 0) * (x - 0)
 return y






def 							A		(       )   ->			int:
 __UpperCamelCase       						= 0.0  # Lower bound of integration
 __UpperCamelCase       						= 1.0  # Upper bound of integration
 __UpperCamelCase       						= 10.0  # define number of steps or resolution
 __UpperCamelCase       						= [a, b]  # define boundary of integration
 __UpperCamelCase       						= method_a(snake_case       ,      snake_case	)
 print(f'y = {y}'	)


if __name__ == "__main__":
      main()





"""simple docstring"""

from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase			:       List[str]									=				{
    "configuration_autoformer": [
        "AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AutoformerConfig",
    ],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase			:       Dict									=				[
          "AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "AutoformerForPrediction",
          "AutoformerModel",
          "AutoformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_autoformer import (
          AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AutoformerConfig,
      )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_autoformer import (
                AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                AutoformerForPrediction,
                AutoformerModel,
                AutoformerPreTrainedModel,
            )

else:
      import sys

      UpperCamelCase			:       int									=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



