final String delim = NumberConversionUtil.findDecimalSeparator(Locale.JAPAN);
        assertEquals(".", delim);
    }

    /**
     * @throws Exception
     */
    public void testFindFractionDelimeter2() throws Exception {
        final String delim = NumberConversionUtil.findDecimalSeparator(Locale.FRANCE);
        assertEquals(",", delim);
    }

    /**
     * @throws Exception

      :
#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
        api_def_map(op_list),
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
        update_docs_called(false) {
  }

#if !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
  tensorflow::ApiDefMap api_def_map TF_GUARDED_BY(lock);
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)
  bool update_docs_called TF_GUARDED_BY(lock);

		setNproc = false
	case "aix":
		nproc = 9
	case "linux":
		nproc = 6
	case "darwin", "dragonfly", "freebsd", "netbsd", "openbsd":
		nproc = 7
	}
	if setNproc {
		var rlim syscall.Rlimit
		if syscall.Getrlimit(nproc, &rlim) == nil {
			max := int(rlim.Cur) / (threads + 5)
			if attempts > max {
				t.Logf("lowering attempts from %d to %d for RLIMIT_NPROC", attempts, max)
				attempts = max
			}
		}
	}

        "Tensor expected to have rank 2 found rank: ", shape.dims());
  }
  int64_t dim_sizes[] = {2, 4};
  for (int i = 0; i < shape.dims(); i++) {
    if (shape.dim_size(i) != dim_sizes[i]) {
      return errors::InvalidArgument("Dim ", i, " expected to be of size ",
                                     dim_sizes[i],
                                     " found: ", shape.dim_size(i));
    }
  }
  return absl::OkStatus();
}

        "_internal-slim-build"
    ]["packages"][TIANGOLO_BUILD_PACKAGE]
    project_config: Dict[str, Any] = config["project"]
    # Get main optional dependencies, extras
    optional_dependencies: Dict[str, List[str]] = metadata.get(
        "optional-dependencies", {}
    )
    # Get custom optional dependencies name to always include in this (non-slim) package

  auto num_dims_analytical = TF_NumDims(analytical_tensor);
  ASSERT_EQ(dims.size(), num_dims_analytical);
  for (int j = 0; j < num_dims_analytical; j++) {
    auto dim_analytical = TF_Dim(analytical_tensor, j);
    ASSERT_EQ(dims[j], dim_analytical);
    num_elem_analytical *= dim_analytical;
  }

  float* danalytical = new float[num_elem_analytical]{0};
  memcpy(&danalytical[0], TF_TensorData(analytical_tensor),

    "scripts/",
    # For a test
    "docs/en/docs/img/favicon.png",
    ]

[tool.tiangolo._internal-slim-build.packages.fastapi-slim.project]
name = "fastapi-slim"

[tool.tiangolo._internal-slim-build.packages.fastapi]
include-optional-dependencies = ["standard"]

[tool.tiangolo._internal-slim-build.packages.fastapi.project.optional-dependencies]
all = [
    # # For the test client
    "httpx >=0.23.0",

= vendor/github.com/go-task/slim-sprig licensed under: =

Copyright (C) 2013-2020 Masterminds

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

    data[j] = j;
  }
  DLManagedTensor dlm_in = {};
  DLTensor* dltensor_in = &dlm_in.dl_tensor;
  dltensor_in->data = data.data();
  dltensor_in->device = {kDLCPU, 0};
  dltensor_in->ndim = static_cast<int32_t>(shape.size());
  dltensor_in->dtype = {kDLFloat, 32, 1};
  dltensor_in->shape = shape.data();
  dltensor_in->strides = strides.data();
  TFE_TensorHandle* handle = TFE_HandleFromDLPack(&dlm_in, status, ctx);
  ASSERT_NE(handle, nullptr)

  // ImmediateExecutionTensorHandles, but comes up for example when computing
  // the shape of a parallel tensor with component shapes differing across
  // devices.
  virtual Status Dim(int dim_index, int64_t* dim) const = 0;

  // Returns the device which created the handle.
  virtual const char* DeviceName(Status* status) const = 0;
  // Returns the device where the tensor was placed.